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1.  The COPD Knowledge Base: enabling data analysis and computational simulation in translational COPD research 
Journal of Translational Medicine  2014;12(Suppl 2):S6.
Background
Previously we generated a chronic obstructive pulmonary disease (COPD) specific knowledge base (http://www.copdknowledgebase.eu) from clinical and experimental data, text-mining results and public databases. This knowledge base allowed the retrieval of specific molecular networks together with integrated clinical and experimental data.
Results
The COPDKB has now been extended to integrate over 40 public data sources on functional interaction (e.g. signal transduction, transcriptional regulation, protein-protein interaction, gene-disease association). In addition we integrated COPD-specific expression and co-morbidity networks connecting over 6 000 genes/proteins with physiological parameters and disease states. Three mathematical models describing different aspects of systemic effects of COPD were connected to clinical and experimental data. We have completely redesigned the technical architecture of the user interface and now provide html and web browser-based access and form-based searches. A network search enables the use of interconnecting information and the generation of disease-specific sub-networks from general knowledge. Integration with the Synergy-COPD Simulation Environment enables multi-scale integrated simulation of individual computational models while integration with a Clinical Decision Support System allows delivery into clinical practice.
Conclusions
The COPD Knowledge Base is the only publicly available knowledge resource dedicated to COPD and combining genetic information with molecular, physiological and clinical data as well as mathematical modelling. Its integrated analysis functions provide overviews about clinical trends and connections while its semantically mapped content enables complex analysis approaches. We plan to further extend the COPDKB by offering it as a repository to publish and semantically integrate data from relevant clinical trials. The COPDKB is freely available after registration at http://www.copdknowledgebase.eu.
doi:10.1186/1479-5876-12-S2-S6
PMCID: PMC4255911  PMID: 25471253
2.  Predictive medicine: outcomes, challenges and opportunities in the Synergy-COPD project 
Journal of Translational Medicine  2014;12(Suppl 2):S12.
Background
Chronic Obstructive Pulmonary Disease (COPD) is a major challenge for healthcare. Heterogeneities in clinical manifestations and in disease progression are relevant traits in COPD with impact on patient management and prognosis. It is hypothesized that COPD heterogeneity results from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering.
Objectives
To assess the potential of systems medicine to better understand non-pulmonary determinants of COPD heterogeneity. To transfer acquired knowledge to healthcare enhancing subject-specific health risk assessment and stratification to improve management of chronic patients.
Method
Underlying mechanisms of skeletal muscle dysfunction and of co-morbidity clustering in COPD patients were explored with strategies combining deterministic modelling and network medicine analyses using the Biobridge dataset. An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was done (ICD9-CM data from Medicare, 13 million people). A targeted network analysis using the two studies: skeletal muscle dysfunction and co-morbidity clustering explored shared pathways between them.
Results
(1) Evidence of abnormal regulation of pivotal skeletal muscle biological pathways and increased risk for co-morbidity clustering was observed in COPD; (2) shared abnormal pathway regulation between skeletal muscle dysfunction and co-morbidity clustering; and, (3) technological achievements of the projects were: (i) COPD Knowledge Base; (ii) novel modelling approaches; (iii) Simulation Environment; and, (iv) three layers of Clinical Decision Support Systems.
Conclusions
The project demonstrated the high potential of a systems medicine approach to address COPD heterogeneity. Limiting factors for the project development were identified. They were relevant to shape strategies fostering 4P Medicine for chronic patients. The concept of Digital Health Framework and the proposed roadmap for its deployment constituted relevant project outcomes.
doi:10.1186/1479-5876-12-S2-S12
PMCID: PMC4255885  PMID: 25472742
chronic diseases; co-morbidities; chronic obstructive pulmonary disease; information and communication technologies; modelling; network medicine; training
3.  A self-evaluation tool for integrated care services: the Development Model for Integrated Care applied in practice 
Purpose
The purpose of the workshop is to show the applications of the Development Model for Integrated Care (DMIC) in practice. This relatively new and validated model, can be used by integrated care practices to evaluate their integrated care, to assess their phase of development and reveal improvement areas. In the workshop the results of the use of the model in three types of integrated care settings in the Netherlands will be presented. Participants are offered practical instruments based on the validated DMIC to use in their own setting and will be introduced to the webbased tool.
Context
To integrate care from multiple providers into a coherent and streamlined client-focused service, a large number of activities and agreements have to be implemented like streamlining information flows and adequate transfers of clients. In the large range of possible activities it is often not clear what essential activities are and where to start or continue. Also, knowledge about how to further develop integrated care services is needed. The Development Model for Integrated Care (DMIC), based on PhD research of Mirella Minkman, describes nine clusters containing in total 89 elements that contribute to the integration of care. The clusters are named: ‘client-centeredness’, ‘delivery system’, ‘performance management’, ‘quality of care’, ‘result-focused learning’, ‘interprofessional teamwork’, ‘roles and tasks’, ‘commitment’, and ‘transparant entrepreneurship’ [1–3]. In 2011 a new digital webbased self-evolution tool which contains the 89 elements grouped in nine clusters was developed. The DMIC also describes four phases of development [4]. The model is empirically validated in practice by assessing the relevance and implementation of the elements and development phases in 84 integrated care services in The Netherlands: in stroke, acute myocardial infarct (AMI), and dementia services. The validation studies are recently published [5, 6]. In 2011 also other integrated care services started using the model [7]. Vilans developed a digital web-based self-evaluation tool for integrated care services based on the DMIC. A palliative care network, four diabetes services, a youth care service and a network for autism used the self-evaluation tool to evaluate the development of their integrated care service. Because of its generic character, the model and tool are believed to be also interesting internationally.
Data sources
In the workshop we will present the results of three studies in integrated diabetes, youth and palliative care. The three projects consist of multiple steps, see below. Workshop participants could also work with the DMIC following these steps.
One: Preparation of the digital self-evolution tool for integrated care services
Although they are very different, the three integrated care services all wanted to gain insight in their development and improvement opportunities. We tailored the digital self-evaluation tool for each specific integrated care services, but for all the basis was the DMIC. Personal accounts for the digital DMIC self-evalution survey were sent to multiple partners working in each integrated care service (4–16 partners).
Two: Use of the online self-evaluation tool each partner of the local integrated care setting evaluated the integrated care by filling in the web-based questionnaire. The tool consists of three parts (A-C) named: general information about the integrated care practice (A); the clusters and elements of the DMIC (B); and the four phases of development (C). The respondents rated the relevance and presence of each element in their integrated care practice. Respondents were asked to estimate in which phase of development their thought their service was.
Three: Analysing the results
Advisers from Vilans, the Centre of excellence for long-term care in the Netherlands, analysed the self-evolution results in cooperation with the integrated care coordinators. The results show the total amount of implemented integrated care elements per cluster in spider graphs and the development phase as calculated by the DMIC model. Suggestions for further development of the integrated care services were analysed and reported.
Four: Discussing the implications for further development
In a workshop with the local integrated care partners the results of the self-evaluation were presented and discussed. We noticed remarkable results and highlight elements for further development. In addition, we gave advice for further development appropriate to the development phase of the integrated care service. Furthermore, the professionals prioritized the elements and decided which elements to start working on. This resulted in a (quality improvement) plan for the further development of the integrated care service.
Five: Reporting results
In a report all the results of the survey (including consensus scores) and the workshops came together. The integrated care coordinators stated that the reports really helped them to assess their improvement strategy. Also, there was insight in the development phase of their service which gave tools for further development.
Case description
The three cases presented are a palliative network, an integrated diabetes services and an integrated care network for youth in the Netherlands. The palliative care network wanted to reflect on their current development, to build a guiding framework for further development of the network. About sixteen professionals within the network worked with the digital self-evaluation tool and the DMIC: home care organisations, welfare organizations, hospice centres, health care organisations, community organizations.
For diabetes care, a Dutch health care insurance company wished to gain insight in the development of the contracted integrated care services to stimulate further development of the services. Professionals of three diabetes integrated care services were invited to fill in the digital self-evaluation tool. Of each integrated care service professionals like a general practitioner, a diabetes nurse, a medical specialist, a dietician and a podiatrist were invited. In youth care, a local health organisation wondered whether the DMIC could be helpful to visualize the results of youth integrated care services at process- and organisational level. The goal of the project was to define indicators at a process- and organisational level for youth care services based on the DMIC. In the future, these indicators might be used to evaluate youth care integrated care services and improve the quality of youth care within the Netherlands.
Conclusions and discussion
It is important for the quality of integrated care services that the involved coordinators, managers and professionals are aware of the development process of the integrated care service and that they focus on elements which can further develop and improve their integrated care. However, we noticed that integrated care services in the Netherlands experience difficulties in developing their integrated care service. It is often not clear what essential activities are to work on and how to further develop the integrated care service. A guiding framework for the development of integrated care was missing. The DMIC model has been developed for that reason and offers a useful tool for assessment, self-evaluation or improvement of integrated care services in practice. The model has been validated for AMI, dementia and stroke services. The latest new studies in diabetes, palliative care and youth care gave further insight in the generic character of the DMIC. Based on these studies it can be assumed that the DMIC can be used for multiple types of integrated care services. The model is assumed to be interesting for an international audience. Improving integrated care is a complex topic in a large number of countries; the DMIC is also based on the international literature. Dutch integrated care coordinators stated that the DMIC helped them to assess their integrated care development in practice and supported them in obtaining ideas for expanding and improving their integrated care activities.
The web-based self-evaluation tool focuses on a process- and organisational level of integrated care. Also, the self assessed development phase can be compared to the development phase as calculated by the DMIC tool. The cases showed this is fruitful input for discussions. When using the tool, the results can also be used in quality policy reports and improvement plans. The web-based tool is being tested at this moment in practice, but in San Marino we can present the latest webversion and demonstrate with a short video how to use the tool and model. During practical exercises in the workshop the participants will experience how the application of the DMIC can work for them in practice or in research. For integrated care researchers and policy makers, the DMIC questionnaire and tool is a promising method for further research and policy plans in integrated care.
PMCID: PMC3617779
development model for integrated care; development of integrated care services; implementation and improvement of integrated care; self evaluation
4.  A Semantic Problem Solving Environment for Integrative Parasite Research: Identification of Intervention Targets for Trypanosoma cruzi 
Background
Research on the biology of parasites requires a sophisticated and integrated computational platform to query and analyze large volumes of data, representing both unpublished (internal) and public (external) data sources. Effective analysis of an integrated data resource using knowledge discovery tools would significantly aid biologists in conducting their research, for example, through identifying various intervention targets in parasites and in deciding the future direction of ongoing as well as planned projects. A key challenge in achieving this objective is the heterogeneity between the internal lab data, usually stored as flat files, Excel spreadsheets or custom-built databases, and the external databases. Reconciling the different forms of heterogeneity and effectively integrating data from disparate sources is a nontrivial task for biologists and requires a dedicated informatics infrastructure. Thus, we developed an integrated environment using Semantic Web technologies that may provide biologists the tools for managing and analyzing their data, without the need for acquiring in-depth computer science knowledge.
Methodology/Principal Findings
We developed a semantic problem-solving environment (SPSE) that uses ontologies to integrate internal lab data with external resources in a Parasite Knowledge Base (PKB), which has the ability to query across these resources in a unified manner. The SPSE includes Web Ontology Language (OWL)-based ontologies, experimental data with its provenance information represented using the Resource Description Format (RDF), and a visual querying tool, Cuebee, that features integrated use of Web services. We demonstrate the use and benefit of SPSE using example queries for identifying gene knockout targets of Trypanosoma cruzi for vaccine development. Answers to these queries involve looking up multiple sources of data, linking them together and presenting the results.
Conclusion/Significance
The SPSE facilitates parasitologists in leveraging the growing, but disparate, parasite data resources by offering an integrative platform that utilizes Semantic Web techniques, while keeping their workload increase minimal.
Author Summary
Effective research in parasite biology requires analyzing experimental lab data in the context of constantly expanding public data resources. Integrating lab data with public resources is particularly difficult for biologists who may not possess significant computational skills to acquire and process heterogeneous data stored at different locations. Therefore, we develop a semantic problem solving environment (SPSE) that allows parasitologists to query their lab data integrated with public resources using ontologies. An ontology specifies a common vocabulary and formal relationships among the terms that describe an organism, and experimental data and processes in this case. SPSE supports capturing and querying provenance information, which is metadata on the experimental processes and data recorded for reproducibility, and includes a visual query-processing tool to formulate complex queries without learning the query language syntax. We demonstrate the significance of SPSE in identifying gene knockout targets for T. cruzi. The overall goal of SPSE is to help researchers discover new or existing knowledge that is implicitly present in the data but not always easily detected. Results demonstrate improved usefulness of SPSE over existing lab systems and approaches, and support for complex query design that is otherwise difficult to achieve without the knowledge of query language syntax.
doi:10.1371/journal.pntd.0001458
PMCID: PMC3260319  PMID: 22272365
5.  Sig2BioPAX: Java tool for converting flat files to BioPAX Level 3 format 
Background
The World Wide Web plays a critical role in enabling molecular, cell, systems and computational biologists to exchange, search, visualize, integrate, and analyze experimental data. Such efforts can be further enhanced through the development of semantic web concepts. The semantic web idea is to enable machines to understand data through the development of protocol free data exchange formats such as Resource Description Framework (RDF) and the Web Ontology Language (OWL). These standards provide formal descriptors of objects, object properties and their relationships within a specific knowledge domain. However, the overhead of converting datasets typically stored in data tables such as Excel, text or PDF into RDF or OWL formats is not trivial for non-specialists and as such produces a barrier to seamless data exchange between researchers, databases and analysis tools. This problem is particularly of importance in the field of network systems biology where biochemical interactions between genes and their protein products are abstracted to networks.
Results
For the purpose of converting biochemical interactions into the BioPAX format, which is the leading standard developed by the computational systems biology community, we developed an open-source command line tool that takes as input tabular data describing different types of molecular biochemical interactions. The tool converts such interactions into the BioPAX level 3 OWL format. We used the tool to convert several existing and new mammalian networks of protein interactions, signalling pathways, and transcriptional regulatory networks into BioPAX. Some of these networks were deposited into PathwayCommons, a repository for consolidating and organizing biochemical networks.
Conclusions
The software tool Sig2BioPAX is a resource that enables experimental and computational systems biologists to contribute their identified networks and pathways of molecular interactions for integration and reuse with the rest of the research community.
doi:10.1186/1751-0473-6-5
PMCID: PMC3071313  PMID: 21418653
6.  BioGateway: a semantic systems biology tool for the life sciences 
BMC Bioinformatics  2009;10(Suppl 10):S11.
Background
Life scientists need help in coping with the plethora of fast growing and scattered knowledge resources. Ideally, this knowledge should be integrated in a form that allows them to pose complex questions that address the properties of biological systems, independently from the origin of the knowledge. Semantic Web technologies prove to be well suited for knowledge integration, knowledge production (hypothesis formulation), knowledge querying and knowledge maintenance.
Results
We implemented a semantically integrated resource named BioGateway, comprising the entire set of the OBO foundry candidate ontologies, the GO annotation files, the SWISS-PROT protein set, the NCBI taxonomy and several in-house ontologies. BioGateway provides a single entry point to query these resources through SPARQL. It constitutes a key component for a Semantic Systems Biology approach to generate new hypotheses concerning systems properties. In the course of developing BioGateway, we faced challenges that are common to other projects that involve large datasets in diverse representations. We present a detailed analysis of the obstacles that had to be overcome in creating BioGateway. We demonstrate the potential of a comprehensive application of Semantic Web technologies to global biomedical data.
Conclusion
The time is ripe for launching a community effort aimed at a wider acceptance and application of Semantic Web technologies in the life sciences. We call for the creation of a forum that strives to implement a truly semantic life science foundation for Semantic Systems Biology.
Access to the system and supplementary information (such as a listing of the data sources in RDF, and sample queries) can be found at .
doi:10.1186/1471-2105-10-S10-S11
PMCID: PMC2755819  PMID: 19796395
7.  Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism 
The first computational approach for the rapid generation of genome-scale tissue-specific models from a generic species model.A genome scale model of human liver metabolism, which is comprehensively tested and validated using cross-validation and the ability to carry out complex hepatic metabolic functions.The model's flux predictions are shown to correlate with flux measurements across a variety of hormonal and dietary conditions, and are successfully used to predict biomarker changes in genetic metabolic disorders, both with higher accuracy than the generic human model.
The study of normal human metabolism and its alterations is central to the understanding and treatment of a variety of human diseases, including diabetes, metabolic syndrome, neurodegenerative disorders, and cancer. A promising systems biology approach for studying human metabolism is through the development and analysis of large-scale stoichiometric network models of human metabolism. The reconstruction of these network models has followed two main paths: the former being the reconstruction of generic (non-tissue specific) models, characterizing the complete metabolic potential of human cells, based mostly on genomic data to trace enzyme-coding genes (Duarte et al, 2007; Ma et al, 2007), and the latter is the reconstruction of cell type- and tissue-specific models (Wiback and Palsson, 2002; Chatziioannou et al, 2003; Vo et al, 2004), based on a similar methodology to that described above, with the extra complexity of manual curation of literature evidence for the cell/system specificity of metabolic enzymes and pathways.
On this background, we present in this study, to the best of our knowledge, the first computational approach for a rapid generation of genome-scale tissue-specific models. The method relies on integrating the previously reconstructed generic human models with a variety of high-throughput molecular ‘omics' data, including transcriptomic, proteomic, metabolomic, and phenotypic data, as well as literature-based knowledge, characterizing the tissue in hand (Figure 1). Hence, it can be readily used to quite rapidly build and use a large array of human tissue-specific models. The resulting model satisfies stoichiometric, mass-balance, and thermodynamic constraints. It serves as a functional metabolic network that can then be used to explore the metabolic state of a tissue under various genetic and physiological conditions, simulating enzymatic inhibition or drug applications through standard constraint-based modeling methods, without requiring additional context-specific molecular data.
We applied this approach to build a genome scale model of liver metabolism, which is then comprehensively tested and validated. The model is shown to be able to simulate complex hepatic metabolic functions, as well as depicting the pathological alterations caused by urea cycle deficiencies. The liver model was applied to predict measured intra-cellular metabolic fluxes given measured metabolite uptake and secretion rates at different hepatic metabolic conditions. The predictions were tested using a comprehensive set of flux measurements performed by (Chan et al, 2003), showing that the liver model obtained more accurate predictions compared to those obtained by the original, generic human model (an overall prediction accuracy of 0.67 versus 0.46). Furthermore, it was applied to identify metabolic biomarkers for liver in-born errors of metabolism—once again, displaying superiority vs. the predictions generated by the generic human model (accuracy of 0.67 versus 0.59).
From a biotechnological standpoint, the liver model generated here can serve as a basis for future studies aiming to optimize the functioning of bio artificial liver devices. The application of the method to rapidly construct metabolic models of other human tissues can obviously lead to many other important clinical insights, e.g., concerning means for metabolic salvage of ischemic heart and brain tissues. Last but not least, the application of the new method is not limited to the realm of human modeling; it can be used to generate tissue models for any multi-tissue organism for which a generic model exists, such as the Mus musculus (Quek and Nielsen, 2008; Sheikh et al, 2005) and the model plant Arabidopsis thaliana (Poolman et al, 2009).
The computational study of human metabolism has been advanced with the advent of the first generic (non-tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue-specific genome-scale models of human metabolism. The algorithm generates a tissue-specific model from the generic human model by integrating a variety of tissue-specific molecular data sources, including literature-based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome-scale stoichiometric model of hepatic metabolism. The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions. The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms.
doi:10.1038/msb.2010.56
PMCID: PMC2964116  PMID: 20823844
constraint based; hepatic; liver; metabolism
8.  Community-Based Multidisciplinary Care for Patients With Stable Chronic Obstructive Pulmonary Disease (COPD) 
Executive Summary
In July 2010, the Medical Advisory Secretariat (MAS) began work on a Chronic Obstructive Pulmonary Disease (COPD) evidentiary framework, an evidence-based review of the literature surrounding treatment strategies for patients with COPD. This project emerged from a request by the Health System Strategy Division of the Ministry of Health and Long-Term Care that MAS provide them with an evidentiary platform on the effectiveness and cost-effectiveness of COPD interventions.
After an initial review of health technology assessments and systematic reviews of COPD literature, and consultation with experts, MAS identified the following topics for analysis: vaccinations (influenza and pneumococcal), smoking cessation, multidisciplinary care, pulmonary rehabilitation, long-term oxygen therapy, noninvasive positive pressure ventilation for acute and chronic respiratory failure, hospital-at-home for acute exacerbations of COPD, and telehealth (including telemonitoring and telephone support). Evidence-based analyses were prepared for each of these topics. For each technology, an economic analysis was also completed where appropriate. In addition, a review of the qualitative literature on patient, caregiver, and provider perspectives on living and dying with COPD was conducted, as were reviews of the qualitative literature on each of the technologies included in these analyses.
The Chronic Obstructive Pulmonary Disease Mega-Analysis series is made up of the following reports, which can be publicly accessed at the MAS website at: http://www.hqontario.ca/en/mas/mas_ohtas_mn.html.
Chronic Obstructive Pulmonary Disease (COPD) Evidentiary Framework
Influenza and Pneumococcal Vaccinations for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Smoking Cessation for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Community-Based Multidisciplinary Care for Patients With Stable Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Pulmonary Rehabilitation for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Long-term Oxygen Therapy for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Noninvasive Positive Pressure Ventilation for Acute Respiratory Failure Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Noninvasive Positive Pressure Ventilation for Chronic Respiratory Failure Patients With Stable Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Hospital-at-Home Programs for Patients With Acute Exacerbations of Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Home Telehealth for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Cost-Effectiveness of Interventions for Chronic Obstructive Pulmonary Disease Using an Ontario Policy Model
Experiences of Living and Dying With COPD: A Systematic Review and Synthesis of the Qualitative Empirical Literature
For more information on the qualitative review, please contact Mita Giacomini at: http://fhs.mcmaster.ca/ceb/faculty_member_giacomini.htm.
For more information on the economic analysis, please visit the PATH website: http://www.path-hta.ca/About-Us/Contact-Us.aspx.
The Toronto Health Economics and Technology Assessment (THETA) collaborative has produced an associated report on patient preference for mechanical ventilation. For more information, please visit the THETA website: http://theta.utoronto.ca/static/contact.
Objective
The objective of this evidence-based analysis was to determine the effectiveness and cost-effectiveness of multidisciplinary care (MDC) compared with usual care (UC, single health care provider) for the treatment of stable chronic obstructive pulmonary disease (COPD).
Clinical Need: Condition and Target Population
Chronic obstructive pulmonary disease is a progressive disorder with episodes of acute exacerbations associated with significant morbidity and mortality. Cigarette smoking is linked causally to COPD in more than 80% of cases. Chronic obstructive pulmonary disease is among the most common chronic diseases worldwide and has an enormous impact on individuals, families, and societies through reduced quality of life and increased health resource utilization and mortality.
The estimated prevalence of COPD in Ontario in 2007 was 708,743 persons.
Technology
Multidisciplinary care involves professionals from a range of disciplines, working together to deliver comprehensive care that addresses as many of the patient’s health care and psychosocial needs as possible.
Two variables are inherent in the concept of a multidisciplinary team: i) the multidisciplinary components such as an enriched knowledge base and a range of clinical skills and experiences, and ii) the team components, which include but are not limited to, communication and support measures. However, the most effective number of team members and which disciplines should comprise the team for optimal effect is not yet known.
Research Question
What is the effectiveness and cost-effectiveness of MDC compared with UC (single health care provider) for the treatment of stable COPD?
Research Methods
Literature Search
Search Strategy
A literature search was performed on July 19, 2010 using OVID MEDLINE, OVID MEDLINE In-Process and Other Non-Indexed Citations, OVID EMBASE, EBSCO Cumulative Index to Nursing & Allied Health Literature (CINAHL), the Wiley Cochrane Library, and the Centre for Reviews and Dissemination database, for studies published from January 1, 1995 until July 2010. Abstracts were reviewed by a single reviewer and, for those studies meeting the eligibility criteria, full-text articles were obtained. Reference lists were also examined for any additional relevant studies not identified through the search.
Inclusion Criteria
health technology assessments, systematic reviews, or randomized controlled trials
studies published between January 1995 and July 2010;
COPD study population
studies comparing MDC (2 or more health care disciplines participating in care) compared with UC (single health care provider)
Exclusion Criteria
grey literature
duplicate publications
non-English language publications
study population less than 18 years of age
Outcomes of Interest
hospital admissions
emergency department (ED) visits
mortality
health-related quality of life
lung function
Quality of Evidence
The quality of each included study was assessed, taking into consideration allocation concealment, randomization, blinding, power/sample size, withdrawals/dropouts, and intention-to-treat analyses.
The quality of the body of evidence was assessed as high, moderate, low, or very low according to the GRADE Working Group criteria. The following definitions of quality were used in grading the quality of the evidence:
Summary of Findings
Six randomized controlled trials were obtained from the literature search. Four of the 6 studies were completed in the United States. The sample size of the 6 studies ranged from 40 to 743 participants, with a mean study sample between 66 and 71 years of age. Only 2 studies characterized the study sample in terms of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) COPD stage criteria, and in general the description of the study population in the other 4 studies was limited. The mean percent predicted forced expiratory volume in 1 second (% predicted FEV1) among study populations was between 32% and 59%. Using this criterion, 3 studies included persons with severe COPD and 2 with moderate COPD. Information was not available to classify the population in the sixth study.
Four studies had MDC treatment groups which included a physician. All studies except 1 reported a respiratory specialist (i.e., respiratory therapist, specialist nurse, or physician) as part of the multidisciplinary team. The UC group was comprised of a single health care practitioner who may or may not have been a respiratory specialist.
A meta-analysis was completed for 5 of the 7 outcome measures of interest including:
health-related quality of life,
lung function,
all-cause hospitalization,
COPD-specific hospitalization, and
mortality.
There was only 1 study contributing to the outcome of all-cause and COPD-specific ED visits which precluded pooling data for these outcomes. Subgroup analyses were not completed either because heterogeneity was not significant or there were a small number of studies that were meta-analysed for the outcome.
Quality of Life
Three studies reported results of quality of life assessment based on the St. George’s Respiratory Questionnaire (SGRQ). A mean decrease in the SGRQ indicates an improvement in quality of life while a mean increase indicates deterioration in quality of life. In all studies the mean change score from baseline to the end time point in the MDC treatment group showed either an improvement compared with the control group or less deterioration compared with the control group. The mean difference in change scores between MDC and UC groups was statistically significant in all 3 studies. The pooled weighted mean difference in total SGRQ score was −4.05 (95% confidence interval [CI], −6.47 to 1.63; P = 0.001). The GRADE quality of evidence was assessed as low for this outcome.
Lung Function
Two studies reported results of the FEV1 % predicted as a measure of lung function. A negative change from baseline infers deterioration in lung function and a positive change from baseline infers an improvement in lung function. The MDC group showed a statistically significant improvement in lung function up to 12 months compared with the UC group (P = 0.01). However this effect is not maintained at 2-year follow-up (P = 0.24). The pooled weighted mean difference in FEV1 percent predicted was 2.78 (95% CI, −1.82 to −7.37). The GRADE quality of evidence was assessed as very low for this outcome indicating that an estimate of effect is uncertain.
Hospital Admissions
All-Cause
Four studies reported results of all-cause hospital admissions in terms of number of persons with at least 1 admission during the follow-up period. Estimates from these 4 studies were pooled to determine a summary estimate. There is a statistically significant 25% relative risk (RR) reduction in all-cause hospitalizations in the MDC group compared with the UC group (P < 0.001). The index of heterogeneity (I2) value is 0%, indicating no statistical heterogeneity between studies. The GRADE quality of evidence was assessed as moderate for this outcome, indicating that further research may change the estimate of effect.
COPD-Specific Hospitalization
Three studies reported results of COPD-specific hospital admissions in terms of number of persons with at least 1 admission during the follow-up period. Estimates from these 3 studies were pooled to determine a summary estimate. There is a statistically significant 33% RR reduction in all-cause hospitalizations in the MDC group compared with the UC group (P = 0.002). The I2 value is 0%, indicating no statistical heterogeneity between studies. The GRADE quality of evidence was assessed as moderate for this outcome, indicating that further research may change the estimate of effect.
Emergency Department Visits
All-Cause
Two studies reported results of all-cause ED visits in terms of number of persons with at least 1 visit during the follow-up period. There is a statistically nonsignificant reduction in all-cause ED visits when data from these 2 studies are pooled (RR, 0.64; 95% CI, 0.31 to −1.33; P = 0.24). The GRADE quality of evidence was assessed as very low for this outcome indicating that an estimate of effect is uncertain.
COPD-Specific
One study reported results of COPD-specific ED visits in terms of number of persons with at least 1 visit during the follow-up period. There is a statistically significant 41% reduction in COPD-specific ED visits when the data from these 2 studies are pooled (RR, 0.59; 95% CI, 0.43−0.81; P < 0.001). The GRADE quality of evidence was assessed as moderate for this outcome.
Mortality
Three studies reported the mortality during the study follow-up period. Estimates from these 3 studies were pooled to determine a summary estimate. There is a statistically nonsignificant reduction in mortality between treatment groups (RR, 0.81; 95% CI, 0.52−1.27; P = 0.36). The I2 value is 19%, indicating low statistical heterogeneity between studies. All studies had a 12-month follow-up period. The GRADE quality of evidence was assessed as low for this outcome.
Conclusions
Significant effect estimates with moderate quality of evidence were found for all-cause hospitalization, COPD-specific hospitalization, and COPD-specific ED visits (Table ES1). A significant estimate with low quality evidence was found for the outcome of quality of life (Table ES2). All other outcome measures were nonsignificant and supported by low or very low quality of evidence.
Summary of Dichotomous Data
Abbreviations: CI, confidence intervals; COPD, chronic obstructive pulmonary disease; n, number.
Summary of Continuous Data
Abbreviations: CI, confidence intervals; FEV1, forced expiratory volume in 1 second; n, number; SGRQ, St. George’s Respiratory Questionnaire.
PMCID: PMC3384374  PMID: 23074433
9.  Computational framework to support integration of biomolecular and clinical data within a translational approach 
BMC Bioinformatics  2013;14:180.
Background
The use of the knowledge produced by sciences to promote human health is the main goal of translational medicine. To make it feasible we need computational methods to handle the large amount of information that arises from bench to bedside and to deal with its heterogeneity. A computational challenge that must be faced is to promote the integration of clinical, socio-demographic and biological data. In this effort, ontologies play an essential role as a powerful artifact for knowledge representation. Chado is a modular ontology-oriented database model that gained popularity due to its robustness and flexibility as a generic platform to store biological data; however it lacks supporting representation of clinical and socio-demographic information.
Results
We have implemented an extension of Chado – the Clinical Module - to allow the representation of this kind of information. Our approach consists of a framework for data integration through the use of a common reference ontology. The design of this framework has four levels: data level, to store the data; semantic level, to integrate and standardize the data by the use of ontologies; application level, to manage clinical databases, ontologies and data integration process; and web interface level, to allow interaction between the user and the system. The clinical module was built based on the Entity-Attribute-Value (EAV) model. We also proposed a methodology to migrate data from legacy clinical databases to the integrative framework. A Chado instance was initialized using a relational database management system. The Clinical Module was implemented and the framework was loaded using data from a factual clinical research database. Clinical and demographic data as well as biomaterial data were obtained from patients with tumors of head and neck. We implemented the IPTrans tool that is a complete environment for data migration, which comprises: the construction of a model to describe the legacy clinical data, based on an ontology; the Extraction, Transformation and Load (ETL) process to extract the data from the source clinical database and load it in the Clinical Module of Chado; the development of a web tool and a Bridge Layer to adapt the web tool to Chado, as well as other applications.
Conclusions
Open-source computational solutions currently available for translational science does not have a model to represent biomolecular information and also are not integrated with the existing bioinformatics tools. On the other hand, existing genomic data models do not represent clinical patient data. A framework was developed to support translational research by integrating biomolecular information coming from different “omics” technologies with patient’s clinical and socio-demographic data. This framework should present some features: flexibility, compression and robustness. The experiments accomplished from a use case demonstrated that the proposed system meets requirements of flexibility and robustness, leading to the desired integration. The Clinical Module can be accessed in http://dcm.ffclrp.usp.br/caib/pg=iptrans.
doi:10.1186/1471-2105-14-180
PMCID: PMC3688149  PMID: 23742129
10.  HyQue: evaluating hypotheses using Semantic Web technologies 
Journal of Biomedical Semantics  2011;2(Suppl 2):S3.
Background
Key to the success of e-Science is the ability to computationally evaluate expert-composed hypotheses for validity against experimental data. Researchers face the challenge of collecting, evaluating and integrating large amounts of diverse information to compose and evaluate a hypothesis. Confronted with rapidly accumulating data, researchers currently do not have the software tools to undertake the required information integration tasks.
Results
We present HyQue, a Semantic Web tool for querying scientific knowledge bases with the purpose of evaluating user submitted hypotheses. HyQue features a knowledge model to accommodate diverse hypotheses structured as events and represented using Semantic Web languages (RDF/OWL). Hypothesis validity is evaluated against experimental and literature-sourced evidence through a combination of SPARQL queries and evaluation rules. Inference over OWL ontologies (for type specifications, subclass assertions and parthood relations) and retrieval of facts stored as Bio2RDF linked data provide support for a given hypothesis. We evaluate hypotheses of varying levels of detail about the genetic network controlling galactose metabolism in Saccharomyces cerevisiae to demonstrate the feasibility of deploying such semantic computing tools over a growing body of structured knowledge in Bio2RDF.
Conclusions
HyQue is a query-based hypothesis evaluation system that can currently evaluate hypotheses about the galactose metabolism in S. cerevisiae. Hypotheses as well as the supporting or refuting data are represented in RDF and directly linked to one another allowing scientists to browse from data to hypothesis and vice versa. HyQue hypotheses and data are available at http://semanticscience.org/projects/hyque.
doi:10.1186/2041-1480-2-S2-S3
PMCID: PMC3102892  PMID: 21624158
11.  linkedISA: semantic representation of ISA-Tab experimental metadata 
BMC Bioinformatics  2014;15(Suppl 14):S4.
Background
Reporting and sharing experimental metadata- such as the experimental design, characteristics of the samples, and procedures applied, along with the analysis results, in a standardised manner ensures that datasets are comprehensible and, in principle, reproducible, comparable and reusable. Furthermore, sharing datasets in formats designed for consumption by humans and machines will also maximize their use. The Investigation/Study/Assay (ISA) open source metadata tracking framework facilitates standards-compliant collection, curation, visualization, storage and sharing of datasets, leveraging on other platforms to enable analysis and publication. The ISA software suite includes several components used in increasingly diverse set of life science and biomedical domains; it is underpinned by a general-purpose format, ISA-Tab, and conversions exist into formats required by public repositories. While ISA-Tab works well mainly as a human readable format, we have also implemented a linked data approach to semantically define the ISA-Tab syntax.
Results
We present a semantic web representation of the ISA-Tab syntax that complements ISA-Tab's syntactic interoperability with semantic interoperability. We introduce the linkedISA conversion tool from ISA-Tab to the Resource Description Framework (RDF), supporting mappings from the ISA syntax to multiple community-defined, open ontologies and capitalising on user-provided ontology annotations in the experimental metadata. We describe insights of the implementation and how annotations can be expanded driven by the metadata. We applied the conversion tool as part of Bio-GraphIIn, a web-based application supporting integration of the semantically-rich experimental descriptions. Designed in a user-friendly manner, the Bio-GraphIIn interface hides most of the complexities to the users, exposing a familiar tabular view of the experimental description to allow seamless interaction with the RDF representation, and visualising descriptors to drive the query over the semantic representation of the experimental design. In addition, we defined queries over the linkedISA RDF representation and demonstrated its use over the linkedISA conversion of datasets from Nature' Scientific Data online publication.
Conclusions
Our linked data approach has allowed us to: 1) make the ISA-Tab semantics explicit and machine-processable, 2) exploit the existing ontology-based annotations in the ISA-Tab experimental descriptions, 3) augment the ISA-Tab syntax with new descriptive elements, 4) visualise and query elements related to the experimental design. Reasoning over ISA-Tab metadata and associated data will facilitate data integration and knowledge discovery.
doi:10.1186/1471-2105-15-S14-S4
PMCID: PMC4255742  PMID: 25472428
12.  Assessing Drug Target Association Using Semantic Linked Data 
PLoS Computational Biology  2012;8(7):e1002574.
The rapidly increasing amount of public data in chemistry and biology provides new opportunities for large-scale data mining for drug discovery. Systematic integration of these heterogeneous sets and provision of algorithms to data mine the integrated sets would permit investigation of complex mechanisms of action of drugs. In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges. We developed a statistical model to assess the association of drug target pairs based on their relation with other linked objects. Validation experiments demonstrate the model can correctly identify known direct drug target pairs with high precision. Indirect drug target pairs (for example drugs which change gene expression level) are also identified but not as strongly as direct pairs. We further calculated the association scores for 157 drugs from 10 disease areas against 1683 human targets, and measured their similarity using a score matrix. The similarity network indicates that drugs from the same disease area tend to cluster together in ways that are not captured by structural similarity, with several potential new drug pairings being identified. This work thus provides a novel, validated alternative to existing drug target prediction algorithms. The web service is freely available at: http://chem2bio2rdf.org/slap.
Author Summary
Modern drug discovery requires the understanding of chemogenomics, the complex interaction of chemical compounds and drugs with a wide variety of protein target and genes in the body. A large amount of data pertaining to such relationships exists in publicly-accessible datasets but it is siloed and thus impossible to use in an integrated fashion. In this work we have integrated and semantically annotated a large amount of public data from a wide range of databases, including compound-gene, drug-drug, protein-protein, drug-side effects and so on, to create a complex network of interactions relating to compounds and protein targets. We developed a statistical algorithm called Semantic Link Association Prediction (SLAP) for predicting “missing links” in this data network: i.e. compound-target interactions for which there is no experimental data but which are statistically probable given the other relationships that exist in this set. We present validation experiments which show this method works with a high degree of accuracy, and also demonstrate how it can be used to create a drug similarity network to make predictions of new indications for existing drugs.
doi:10.1371/journal.pcbi.1002574
PMCID: PMC3390390  PMID: 22859915
13.  Experiences of Living and Dying With COPD 
Executive Summary
In July 2010, the Medical Advisory Secretariat (MAS) began work on a Chronic Obstructive Pulmonary Disease (COPD) evidentiary framework, an evidence-based review of the literature surrounding treatment strategies for patients with COPD. This project emerged from a request by the Health System Strategy Division of the Ministry of Health and Long-Term Care that MAS provide them with an evidentiary platform on the effectiveness and cost-effectiveness of COPD interventions.
After an initial review of health technology assessments and systematic reviews of COPD literature, and consultation with experts, MAS identified the following topics for analysis: vaccinations (influenza and pneumococcal), smoking cessation, multidisciplinary care, pulmonary rehabilitation, long-term oxygen therapy, noninvasive positive pressure ventilation for acute and chronic respiratory failure, hospital-at-home for acute exacerbations of COPD, and telehealth (including telemonitoring and telephone support). Evidence-based analyses were prepared for each of these topics. For each technology, an economic analysis was also completed where appropriate. In addition, a review of the qualitative literature on patient, caregiver, and provider perspectives on living and dying with COPD was conducted, as were reviews of the qualitative literature on each of the technologies included in these analyses.
The Chronic Obstructive Pulmonary Disease Mega-Analysis series is made up of the following reports, which can be publicly accessed at the MAS website at: http://www.hqontario.ca/en/mas/mas_ohtas_mn.html.
Chronic Obstructive Pulmonary Disease (COPD) Evidentiary Framework
Influenza and Pneumococcal Vaccinations for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Smoking Cessation for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Community-Based Multidisciplinary Care for Patients With Stable Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Pulmonary Rehabilitation for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Long-Term Oxygen Therapy for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Noninvasive Positive Pressure Ventilation for Acute Respiratory Failure Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Noninvasive Positive Pressure Ventilation for Chronic Respiratory Failure Patients With Stable Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Hospital-at-Home Programs for Patients With Acute Exacerbations of Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Home Telehealth for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Cost-Effectiveness of Interventions for Chronic Obstructive Pulmonary Disease Using an Ontario Policy Model
Experiences of Living and Dying With COPD: A Systematic Review and Synthesis of the Qualitative Empirical Literature
For more information on the qualitative review, please contact Mita Giacomini at: http://fhs.mcmaster.ca/ceb/faculty_member_giacomini.htm.
For more information on the economic analysis, please visit the PATH website: http://www.path-hta.ca/About-Us/Contact-Us.aspx.
The Toronto Health Economics and Technology Assessment (THETA) collaborative has produced an associated report on patient preference for mechanical ventilation. For more information, please visit the THETA website: http://theta.utoronto.ca/static/contact.
Objective of Analysis
The objective of this analysis was to review empirical qualitative research on the experiences of patients with chronic obstructive pulmonary disease (COPD), informal caregivers (“carers”), and health care providers—from the point of diagnosis, through daily living and exacerbation episodes, to the end of life.
Clinical Need and Target Population
Qualitative empirical studies (from social sciences, clinical, and related fields) can offer important information about how patients experience their condition. This exploration of the qualitative literature offers insights into patients’ perspectives on COPD, their needs, and how interventions might affect their experiences. The experiences of caregivers are also explored.
Research Question
What do patients with COPD, their informal caregivers (“carers”), and health care providers experience over the course of COPD?
Research Methods
Literature Search
Search Strategy
Literature searches for studies published from January 1, 2000, to November 2010 were performed on November 29, 2010, using OVID MEDLINE; on November 26, 2010, using ISI Web of Science; and on November 28, 2010, using EBSCO Cumulative Index to Nursing and Allied Health Literature (CINAHL). Titles and abstracts were reviewed by a single reviewer and, for those studies meeting the eligibility criteria, full-text articles were obtained. One additional report, highly relevant to the synthesis, appeared in early 2011 during the drafting of this analysis and was included post hoc.
Inclusion Criteria
English-language full reports
studies published between January 1, 2000, and November 2010
primary qualitative empirical research (using any descriptive or interpretive qualitative methodology, including the qualitative component of mixed-methods studies) and secondary syntheses of primary qualitative empirical research
studies addressing any aspect of the experiences of living or dying with COPD from the perspective of persons at risk, patients, health care providers, or informal carers; studies addressing multiple conditions were included if COPD was addressed explicitly
Exclusion Criteria
studies addressing topics other than the experiences of living or dying with COPD from the perspective of persons at risk, patients, health care providers, or informal carers
studies labelled “qualitative” but not using a qualitative descriptive or interpretive methodology (e.g., case studies, experiments, or observational analysis using qualitative categorical variables)
quantitative research (i.e., using statistical hypothesis testing, using primarily quantitative data or analyses, or expressing results in quantitative or statistical terms)
studies that did not pose an empirical research objective or question, or involve the primary or secondary analysis of empirical data
Outcomes of Interest
qualitative descriptions and interpretations (narrative or theoretical) of personal and social experiences of COPD
Summary of Findings
Experiences at Diagnosis
Patients typically seek initial treatment for an acute episode rather than for chronic early symptoms of COPD.
Many patients initially misunderstand terms such as COPD, chronic obstructive pulmonary disease, or exacerbation.
Patients may not realize that COPD is incurable and fatal; some physicians themselves do not consider early COPD to be a fatal disease.
Smokers may not readily understand or agree with the idea that smoking caused or worsens their COPD. Those who believe there is a causal link may feel regret or shame.
Experiences of Living Day to Day
COPD patients experience alternating good days and bad days. A roller-coaster pattern of ups and downs becomes apparent, and COPD becomes a way of life.
Patients use many means (social, psychological, medical, organizational) to control what they can, and to cope with what they cannot. Economic hardship, comorbidities, language barriers, and low health literacy can make coping more difficult.
Increasing vulnerability and unpredictable setbacks make patients dependent on others for practical assistance, but functional limitations, institutional living or self-consciousness can isolate patients from the people they need.
For smokers, medical advice to quit can conflict with increased desire to smoke as a coping strategy.
Many of the factors that isolate COPD patients from social contact also isolate them from health care.
Experiences of Exacerbations
Patients may not always attribute repeated exacerbations to advancing disease, instead seeing them as temporary setbacks caused by activities, environmental factors, faltering self-management, or infection.
Lack of confidence in community-based services leads some patients to seek hospital admission, but patients also feel vulnerable when hospitalized. They may feel dependent on others for care or traumatized by hospital care routines.
Upon hospital discharge following an exacerbation, patients may face new levels of uncertainty about their illness, prognosis, care providers, and supports.
Experiences of the End of Life
Patients tend to be poorly informed about the long-term prognosis of COPD and what to expect toward the end of life; this lack of understanding impairs quality of life as the disease progresses.
As the end of life approaches, COPD patients face the usual challenges of daily living, but in a context of increasing exacerbations and deepening dependency. Activities and mobility decrease, and life may become confined.
Some clinicians have difficulty identifying the beginning of “the end of life,” given the unpredictable course of COPD. Long-term physician-patient relationships, familiarity and understanding, trust, good communication skills, sensitivity, and secure discussion settings can help facilitate end-of-life discussions.
Divergent meanings and goals of palliative care in COPD lead to confusion about whether such services are the responsibility of home care, primary care, specialty care, or even critical care. Palliative end-of-life care may not be anticipated prior to referral for such care. A palliative care referral can convey the demoralizing message that providers have “given up.”
Experiences of Carers
Carers’ challenges often echo patients’ challenges, and include anxiety, uncertainty about the future, helplessness, powerlessness, depression, difficulties maintaining employment, loss of mobility and freedoms, strained relationships, and growing social isolation.
Carers feel pressured by their many roles, struggling to maintain patience when they feel overwhelmed, and often feeling guilty about not doing enough.
Carers often face their own health problems and may have difficulty sustaining employment.
Synthesis: A Disease Trajectory Reflecting Patient Experiences
The flux of needs in COPD calls for service continuity and flexibility to allow both health care providers and patients to respond to the unpredictable yet increasing demands of the disease over time.
PMCID: PMC3384365  PMID: 23074423
14.  atBioNet– an integrated network analysis tool for genomics and biomarker discovery 
BMC Genomics  2012;13:325.
Background
Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity.
Results
atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis.
Conclusion
atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.
doi:10.1186/1471-2164-13-325
PMCID: PMC3443675  PMID: 22817640
Protein-protein interaction; Network analysis; Functional module; Disease biomarker; KEGG pathway analysis; Visualization tool; Genomics
15.  Home Telehealth for Patients With Chronic Obstructive Pulmonary Disease (COPD) 
Executive Summary
In July 2010, the Medical Advisory Secretariat (MAS) began work on a Chronic Obstructive Pulmonary Disease (COPD) evidentiary framework, an evidence-based review of the literature surrounding treatment strategies for patients with COPD. This project emerged from a request by the Health System Strategy Division of the Ministry of Health and Long-Term Care that MAS provide them with an evidentiary platform on the effectiveness and cost-effectiveness of COPD interventions.
After an initial review of health technology assessments and systematic reviews of COPD literature, and consultation with experts, MAS identified the following topics for analysis: vaccinations (influenza and pneumococcal), smoking cessation, multidisciplinary care, pulmonary rehabilitation, long-term oxygen therapy, noninvasive positive pressure ventilation for acute and chronic respiratory failure, hospital-at-home for acute exacerbations of COPD, and telehealth (including telemonitoring and telephone support). Evidence-based analyses were prepared for each of these topics. For each technology, an economic analysis was also completed where appropriate. In addition, a review of the qualitative literature on patient, caregiver, and provider perspectives on living and dying with COPD was conducted, as were reviews of the qualitative literature on each of the technologies included in these analyses.
The Chronic Obstructive Pulmonary Disease Mega-Analysis series is made up of the following reports, which can be publicly accessed at the MAS website at: http://www.hqontario.ca/en/mas/mas_ohtas_mn.html.
Chronic Obstructive Pulmonary Disease (COPD) Evidentiary Framework
Influenza and Pneumococcal Vaccinations for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Smoking Cessation for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Community-Based Multidisciplinary Care for Patients With Stable Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Pulmonary Rehabilitation for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Long-term Oxygen Therapy for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Noninvasive Positive Pressure Ventilation for Acute Respiratory Failure Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Noninvasive Positive Pressure Ventilation for Chronic Respiratory Failure Patients With Stable Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Hospital-at-Home Programs for Patients With Acute Exacerbations of Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Home Telehealth for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Cost-Effectiveness of Interventions for Chronic Obstructive Pulmonary Disease Using an Ontario Policy Model
Experiences of Living and Dying With COPD: A Systematic Review and Synthesis of the Qualitative Empirical Literature
For more information on the qualitative review, please contact Mita Giacomini at: http://fhs.mcmaster.ca/ceb/faculty_member_giacomini.htm.
For more information on the economic analysis, please visit the PATH website: http://www.path-hta.ca/About-Us/Contact-Us.aspx.
The Toronto Health Economics and Technology Assessment (THETA) collaborative has produced an associated report on patient preference for mechanical ventilation. For more information, please visit the THETA website: http://theta.utoronto.ca/static/contact.
Objective
The objective of this analysis was to conduct an evidence-based assessment of home telehealth technologies for patients with chronic obstructive pulmonary disease (COPD) in order to inform recommendations regarding the access and provision of these services in Ontario. This analysis was one of several analyses undertaken to evaluate interventions for COPD. The perspective of this assessment was that of the Ontario Ministry of Health and Long-Term Care, a provincial payer of medically necessary health care services.
Clinical Need: Condition and Target Population
Canada is facing an increase in chronic respiratory diseases due in part to its aging demographic. The projected increase in COPD will put a strain on health care payers and providers. There is therefore an increasing demand for telehealth services that improve access to health care services while maintaining or improving quality and equality of care. Many telehealth technologies however are in the early stages of development or diffusion and thus require study to define their application and potential harms or benefits. The Medical Advisory Secretariat (MAS) therefore sought to evaluate telehealth technologies for COPD.
Technology
Telemedicine (or telehealth) refers to using advanced information and communication technologies and electronic medical devices to support the delivery of clinical care, professional education, and health-related administrative services.
Generally there are 4 broad functions of home telehealth interventions for COPD:
to monitor vital signs or biological health data (e.g., oxygen saturation),
to monitor symptoms, medication, or other non-biologic endpoints (e.g., exercise adherence),
to provide information (education) and/or other support services (such as reminders to exercise or positive reinforcement), and
to establish a communication link between patient and provider.
These functions often require distinct technologies, although some devices can perform a number of these diverse functions. For the purposes of this review, MAS focused on home telemonitoring and telephone only support technologies.
Telemonitoring (or remote monitoring) refers to the use of medical devices to remotely collect a patient’s vital signs and/or other biologic health data and the transmission of those data to a monitoring station for interpretation by a health care provider.
Telephone only support refers to disease/disorder management support provided by a health care provider to a patient who is at home via telephone or videoconferencing technology in the absence of transmission of patient biologic data.
Research Questions
What is the effectiveness, cost-effectiveness, and safety of home telemonitoring compared with usual care for patients with COPD?
What is the effectiveness, cost-effectiveness, and safety of telephone only support programs compared with usual care for patients with COPD?
Research Methods
Literature Search
Search Strategy
A literature search was performed on November 3, 2010 using OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, the Cumulative Index to Nursing & Allied Health Literature (CINAHL), the Cochrane Library, and the International Agency for Health Technology Assessment (INAHTA) for studies published from January 1, 2000 until November 3, 2010. Abstracts were reviewed by a single reviewer and, for those studies meeting the eligibility criteria, full-text articles were obtained. Reference lists were also examined for any additional relevant studies not identified through the search. Articles with unknown eligibility were reviewed with a second clinical epidemiologist, and then a group of epidemiologists until consensus was established. The quality of evidence was assessed as high, moderate, low, or very low according to GRADE methodology.
Inclusion Criteria – Question #1
frequent transmission of a patient’s physiological data collected at home and without a health care professional physically present to health care professionals for routine monitoring through the use of a communication technology;
monitoring combined with a coordinated management and feedback system based on transmitted data;
telemonitoring as a key component of the intervention (subjective determination);
usual care as provided by the usual care provider for the control group;
randomized controlled trials (RCTs), controlled clinical trials (CCTs), systematic reviews, and/or meta-analyses;
published between January 1, 2000 and November 3, 2010.
Inclusion Criteria – Question #2
scheduled or frequent contact between patient and a health care professional via telephone or videoconferencing technology in the absence of transmission of patient physiological data;
monitoring combined with a coordinated management and feedback system based on transmitted data;
telephone support as a key component of the intervention (subjective determination);
usual care as provided by the usual care provider for the control group;
RCTs, CCTs, systematic reviews, and/or meta-analyses;
published between January 1, 2000 and November 3, 2010.
Exclusion Criteria
published in a language other than English;
intervention group (and not control) receiving some form of home visits by a medical professional, typically a nurse (i.e., telenursing) beyond initial technology set-up and education, to collect physiological data, or to somehow manage or treat the patient;
not recording patient or health system outcomes (e.g., technical reports testing accuracy, reliability or other development-related outcomes of a device, acceptability/feasibility studies, etc.);
not using an independent control group that received usual care (e.g., studies employing historical or periodic controls).
Outcomes of Interest
hospitalizations (primary outcome)
mortality
emergency department visits
length of stay
quality of life
other […]
Subgroup Analyses (a priori)
length of intervention (primary)
severity of COPD (primary)
Quality of Evidence
The quality of evidence assigned to individual studies was determined using a modified CONSORT Statement Checklist for Randomized Controlled Trials. (1) The CONSORT Statement was adapted to include 3 additional quality measures: the adequacy of control group description, significant differential loss to follow-up between groups, and greater than or equal to 30% study attrition. Individual study quality was defined based on total scores according to the CONSORT Statement checklist: very low (0 to < 40%), low (≥ 40 to < 60%), moderate (≥ 60 to < 80%), and high (≥ 80 to 100%).
The quality of the body of evidence was assessed as high, moderate, low, or very low according to the GRADE Working Group criteria. The following definitions of quality were used in grading the quality of the evidence:
Summary of Findings
Six publications, representing 5 independent trials, met the eligibility criteria for Research Question #1. Three trials were RCTs reported across 4 publications, whereby patients were randomized to home telemonitoring or usual care, and 2 trials were CCTs, whereby patients or health care centers were nonrandomly assigned to intervention or usual care.
A total of 310 participants were studied across the 5 included trials. The mean age of study participants in the included trials ranged from 61.2 to 74.5 years for the intervention group and 61.1 to 74.5 years for the usual care group. The percentage of men ranged from 40% to 64% in the intervention group and 46% to 72% in the control group.
All 5 trials were performed in a moderate to severe COPD patient population. Three trials initiated the intervention following discharge from hospital. One trial initiated the intervention following a pulmonary rehabilitation program. The final trial initiated the intervention during management of patients at an outpatient clinic.
Four of the 5 trials included oxygen saturation (i.e., pulse oximetry) as one of the biological patient parameters being monitored. Additional parameters monitored included forced expiratory volume in one second, peak expiratory flow, and temperature.
There was considerable clinical heterogeneity between trials in study design, methods, and intervention/control. In relation to the telemonitoring intervention, 3 of the 5 included studies used an electronic health hub that performed multiple functions beyond the monitoring of biological parameters. One study used only a pulse oximeter device alone with modem capabilities. Finally, in 1 study, patients measured and then forwarded biological data to a nurse during a televideo consultation. Usual care varied considerably between studies.
Only one trial met the eligibility criteria for Research Question #2. The included trial was an RCT that randomized 60 patients to nurse telephone follow-up or usual care (no telephone follow-up). Participants were recruited from the medical department of an acute-care hospital in Hong Kong and began receiving follow-up after discharge from the hospital with a diagnosis of COPD (no severity restriction). The intervention itself consisted of only two 10-to 20-minute telephone calls, once between days 3 to 7 and once between days 14 to 20, involving a structured, individualized educational and supportive programme led by a nurse that focused on 3 components: assessment, management options, and evaluation.
Regarding Research Question #1:
Low to very low quality evidence (according to GRADE) finds non-significant effects or conflicting effects (of significant or non-significant benefit) for all outcomes examined when comparing home telemonitoring to usual care.
There is a trend towards significant increase in time free of hospitalization and use of other health care services with home telemonitoring, but these findings need to be confirmed further in randomized trials of high quality.
There is severe clinical heterogeneity between studies that limits summary conclusions.
The economic impact of home telemonitoring is uncertain and requires further study.
Home telemonitoring is largely dependent on local information technologies, infrastructure, and personnel, and thus the generalizability of external findings may be low. Jurisdictions wishing to replicate home telemonitoring interventions should likely test those interventions within their jurisdictional framework before adoption, or should focus on home-grown interventions that are subjected to appropriate evaluation and proven effective.
Regarding Research Question #2:
Low quality evidence finds significant benefit in favour of telephone-only support for self-efficacy and emergency department visits when compared to usual care, but non-significant results for hospitalizations and hospital length of stay.
There are very serious issues with the generalizability of the evidence and thus additional research is required.
PMCID: PMC3384362  PMID: 23074421
16.  Smoking Cessation for Patients With Chronic Obstructive Pulmonary Disease (COPD) 
Executive Summary
In July 2010, the Medical Advisory Secretariat (MAS) began work on a Chronic Obstructive Pulmonary Disease (COPD) evidentiary framework, an evidence-based review of the literature surrounding treatment strategies for patients with COPD. This project emerged from a request by the Health System Strategy Division of the Ministry of Health and Long-Term Care that MAS provide them with an evidentiary platform on the effectiveness and cost-effectiveness of COPD interventions.
After an initial review of health technology assessments and systematic reviews of COPD literature, and consultation with experts, MAS identified the following topics for analysis: vaccinations (influenza and pneumococcal), smoking cessation, multidisciplinary care, pulmonary rehabilitation, long-term oxygen therapy, noninvasive positive pressure ventilation for acute and chronic respiratory failure, hospital-at-home for acute exacerbations of COPD, and telehealth (including telemonitoring and telephone support). Evidence-based analyses were prepared for each of these topics. For each technology, an economic analysis was also completed where appropriate. In addition, a review of the qualitative literature on patient, caregiver, and provider perspectives on living and dying with COPD was conducted, as were reviews of the qualitative literature on each of the technologies included in these analyses.
The Chronic Obstructive Pulmonary Disease Mega-Analysis series is made up of the following reports, which can be publicly accessed at the MAS website at: http://www.hqontario.ca/en/mas/mas_ohtas_mn.html.
Chronic Obstructive Pulmonary Disease (COPD) Evidentiary Framework
Influenza and Pneumococcal Vaccinations for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Smoking Cessation for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Community-Based Multidisciplinary Care for Patients With Stable Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Pulmonary Rehabilitation for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Long-term Oxygen Therapy for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Noninvasive Positive Pressure Ventilation for Acute Respiratory Failure Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Noninvasive Positive Pressure Ventilation for Chronic Respiratory Failure Patients With Stable Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Hospital-at-Home Programs for Patients With Acute Exacerbations of Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Home Telehealth for Patients With Chronic Obstructive Pulmonary Disease (COPD): An Evidence-Based Analysis
Cost-Effectiveness of Interventions for Chronic Obstructive Pulmonary Disease Using an Ontario Policy Model
Experiences of Living and Dying With COPD: A Systematic Review and Synthesis of the Qualitative Empirical Literature
For more information on the qualitative review, please contact Mita Giacomini at: http://fhs.mcmaster.ca/ceb/faculty member_giacomini.htm.
For more information on the economic analysis, please visit the PATH website: http://www.path-hta.ca/About-Us/Contact-Us.aspx.
The Toronto Health Economics and Technology Assessment (THETA) collaborative has produced an associated report on patient preference for mechanical ventilation. For more information, please visit the THETA website: http://theta.utoronto.ca/static/contact.
Objective
The objective of this evidence-based analysis was to determine the effectiveness and cost-effectiveness of smoking cessation interventions in the management of chronic obstructive pulmonary disease (COPD).
Clinical Need: Condition and Target Population
Tobacco smoking is the main risk factor for COPD. It is estimated that 50% of older smokers develop COPD and more than 80% of COPD-associated morbidity is attributed to tobacco smoking. According to the Canadian Community Health Survey, 38.5% of Ontarians who smoke have COPD. In patients with a significant history of smoking, COPD is usually present with symptoms of progressive dyspnea (shortness of breath), cough, and sputum production. Patients with COPD who smoke have a particularly high level of nicotine dependence, and about 30.4% to 43% of patients with moderate to severe COPD continue to smoke. Despite the severe symptoms that COPD patients suffer, the majority of patients with COPD are unable to quit smoking on their own; each year only about 1% of smokers succeed in quitting on their own initiative.
Technology
Smoking cessation is the process of discontinuing the practice of inhaling a smoked substance. Smoking cessation can help to slow or halt the progression of COPD. Smoking cessation programs mainly target tobacco smoking, but may also encompass other substances that can be difficult to stop smoking due to the development of strong physical addictions or psychological dependencies resulting from their habitual use.
Smoking cessation strategies include both pharmacological and nonpharmacological (behavioural or psychosocial) approaches. The basic components of smoking cessation interventions include simple advice, written self-help materials, individual and group behavioural support, telephone quit lines, nicotine replacement therapy (NRT), and antidepressants. As nicotine addiction is a chronic, relapsing condition that usually requires several attempts to overcome, cessation support is often tailored to individual needs, while recognizing that in general, the more intensive the support, the greater the chance of success. Success at quitting smoking decreases in relation to:
a lack of motivation to quit,
a history of smoking more than a pack of cigarettes a day for more than 10 years,
a lack of social support, such as from family and friends, and
the presence of mental health disorders (such as depression).
Research Question
What are the effectiveness and cost-effectiveness of smoking cessation interventions compared with usual care for patients with COPD?
Research Methods
Literature Search
Search Strategy
A literature search was performed on June 24, 2010 using OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations (1950 to June Week 3 2010), EMBASE (1980 to 2010 Week 24), the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, and the Centre for Reviews and Dissemination for studies published between 1950 and June 2010. A single reviewer reviewed the abstracts and obtained full-text articles for those studies meeting the eligibility criteria. Reference lists were also examined for any additional relevant studies not identified through the search. Data were extracted using a standardized data abstraction form.
Inclusion Criteria
English-language, full reports from 1950 to week 3 of June, 2010;
either randomized controlled trials (RCTs), systematic reviews and meta-analyses, or non-RCTs with controls;
a proven diagnosis of COPD;
adult patients (≥ 18 years);
a smoking cessation intervention that comprised at least one of the treatment arms;
≥ 6 months’ abstinence as an outcome; and
patients followed for ≥ 6 months.
Exclusion Criteria
case reports
case series
Outcomes of Interest
≥ 6 months’ abstinence
Quality of Evidence
The quality of each included study was assessed taking into consideration allocation concealment, randomization, blinding, power/sample size, withdrawals/dropouts, and intention-to-treat analyses.
The quality of the body of evidence was assessed as high, moderate, low, or very low according to the GRADE Working Group criteria. The following definitions of quality were used in grading the quality of the evidence:
Summary of Findings
Nine RCTs were identified from the literature search. The sample sizes ranged from 74 to 5,887 participants. A total of 8,291 participants were included in the nine studies. The mean age of the patients in the studies ranged from 54 to 64 years. The majority of studies used the Global Initiative for Chronic Obstructive Lung Disease (GOLD) COPD staging criteria to stage the disease in study subjects. Studies included patients with mild COPD (2 studies), mild-moderate COPD (3 studies), moderate–severe COPD (1 study) and severe–very severe COPD (1 study). One study included persons at risk of COPD in addition to those with mild, moderate, or severe COPD, and 1 study did not define the stages of COPD. The individual quality of the studies was high. Smoking cessation interventions varied across studies and included counselling or pharmacotherapy or a combination of both. Two studies were delivered in a hospital setting, whereas the remaining 7 studies were delivered in an outpatient setting. All studies reported a usual care group or a placebo-controlled group (for the drug-only trials). The follow-up periods ranged from 6 months to 5 years. Due to excessive clinical heterogeneity in the interventions, studies were first grouped into categories of similar interventions; statistical pooling was subsequently performed, where appropriate. When possible, pooled estimates using relative risks for abstinence rates with 95% confidence intervals were calculated. The remaining studies were reported separately.
Abstinence Rates
Table ES1 provides a summary of the pooled estimates for abstinence, at longest follow-up, from the trials included in this review. It also shows the respective GRADE qualities of evidence.
Summary of Results*
Abbreviations: CI, confidence interval; NRT, nicotine replacement therapy.
Statistically significant (P < 0.05).
One trial used in this comparison had 2 treatment arms each examining a different antidepressant.
Conclusions
Based on a moderate quality of evidence, compared with usual care, abstinence rates are significantly higher in COPD patients receiving intensive counselling or a combination of intensive counselling and NRT.
Based on limited and moderate quality of evidence, abstinence rates are significantly higher in COPD patients receiving NRT compared with placebo.
Based on a moderate quality of evidence, abstinence rates are significantly higher in COPD patients receiving the antidepressant bupropion compared to placebo.
PMCID: PMC3384371  PMID: 23074432
17.  The BioLexicon: a large-scale terminological resource for biomedical text mining 
BMC Bioinformatics  2011;12:397.
Background
Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.
Results
This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard.
Conclusions
The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring.
doi:10.1186/1471-2105-12-397
PMCID: PMC3228855  PMID: 21992002
18.  Enriching a biomedical event corpus with meta-knowledge annotation 
BMC Bioinformatics  2011;12:393.
Background
Biomedical papers contain rich information about entities, facts and events of biological relevance. To discover these automatically, we use text mining techniques, which rely on annotated corpora for training. In order to extract protein-protein interactions, genotype-phenotype/gene-disease associations, etc., we rely on event corpora that are annotated with classified, structured representations of important facts and findings contained within text. These provide an important resource for the training of domain-specific information extraction (IE) systems, to facilitate semantic-based searching of documents. Correct interpretation of these events is not possible without additional information, e.g., does an event describe a fact, a hypothesis, an experimental result or an analysis of results? How confident is the author about the validity of her analyses? These and other types of information, which we collectively term meta-knowledge, can be derived from the context of the event.
Results
We have designed an annotation scheme for meta-knowledge enrichment of biomedical event corpora. The scheme is multi-dimensional, in that each event is annotated for 5 different aspects of meta-knowledge that can be derived from the textual context of the event. Textual clues used to determine the values are also annotated. The scheme is intended to be general enough to allow integration with different types of bio-event annotation, whilst being detailed enough to capture important subtleties in the nature of the meta-knowledge expressed in the text. We report here on both the main features of the annotation scheme, as well as its application to the GENIA event corpus (1000 abstracts with 36,858 events). High levels of inter-annotator agreement have been achieved, falling in the range of 0.84-0.93 Kappa.
Conclusion
By augmenting event annotations with meta-knowledge, more sophisticated IE systems can be trained, which allow interpretative information to be specified as part of the search criteria. This can assist in a number of important tasks, e.g., finding new experimental knowledge to facilitate database curation, enabling textual inference to detect entailments and contradictions, etc. To our knowledge, our scheme is unique within the field with regards to the diversity of meta-knowledge aspects annotated for each event.
doi:10.1186/1471-2105-12-393
PMCID: PMC3222636  PMID: 21985429
19.  Overview of the BioCreative III Workshop 
BMC Bioinformatics  2011;12(Suppl 8):S1.
Background
The overall goal of the BioCreative Workshops is to promote the development of text mining and text processing tools which are useful to the communities of researchers and database curators in the biological sciences. To this end BioCreative I was held in 2004, BioCreative II in 2007, and BioCreative II.5 in 2009. Each of these workshops involved humanly annotated test data for several basic tasks in text mining applied to the biomedical literature. Participants in the workshops were invited to compete in the tasks by constructing software systems to perform the tasks automatically and were given scores based on their performance. The results of these workshops have benefited the community in several ways. They have 1) provided evidence for the most effective methods currently available to solve specific problems; 2) revealed the current state of the art for performance on those problems; 3) and provided gold standard data and results on that data by which future advances can be gauged. This special issue contains overview papers for the three tasks of BioCreative III.
Results
The BioCreative III Workshop was held in September of 2010 and continued the tradition of a challenge evaluation on several tasks judged basic to effective text mining in biology, including a gene normalization (GN) task and two protein-protein interaction (PPI) tasks. In total the Workshop involved the work of twenty-three teams. Thirteen teams participated in the GN task which required the assignment of EntrezGene IDs to all named genes in full text papers without any species information being provided to a system. Ten teams participated in the PPI article classification task (ACT) requiring a system to classify and rank a PubMed® record as belonging to an article either having or not having “PPI relevant” information. Eight teams participated in the PPI interaction method task (IMT) where systems were given full text documents and were required to extract the experimental methods used to establish PPIs and a text segment supporting each such method. Gold standard data was compiled for each of these tasks and participants competed in developing systems to perform the tasks automatically.
BioCreative III also introduced a new interactive task (IAT), run as a demonstration task. The goal was to develop an interactive system to facilitate a user’s annotation of the unique database identifiers for all the genes appearing in an article. This task included ranking genes by importance (based preferably on the amount of described experimental information regarding genes). There was also an optional task to assist the user in finding the most relevant articles about a given gene. For BioCreative III, a user advisory group (UAG) was assembled and played an important role 1) in producing some of the gold standard annotations for the GN task, 2) in critiquing IAT systems, and 3) in providing guidance for a future more rigorous evaluation of IAT systems. Six teams participated in the IAT demonstration task and received feedback on their systems from the UAG group. Besides innovations in the GN and PPI tasks making them more realistic and practical and the introduction of the IAT task, discussions were begun on community data standards to promote interoperability and on user requirements and evaluation metrics to address utility and usability of systems.
Conclusions
In this paper we give a brief history of the BioCreative Workshops and how they relate to other text mining competitions in biology. This is followed by a synopsis of the three tasks GN, PPI, and IAT in BioCreative III with figures for best participant performance on the GN and PPI tasks. These results are discussed and compared with results from previous BioCreative Workshops and we conclude that the best performing systems for GN, PPI-ACT and PPI-IMT in realistic settings are not sufficient for fully automatic use. This provides evidence for the importance of interactive systems and we present our vision of how best to construct an interactive system for a GN or PPI like task in the remainder of the paper.
doi:10.1186/1471-2105-12-S8-S1
PMCID: PMC3269932  PMID: 22151647
20.  Network-based analysis reveals distinct association patterns in a semantic MEDLINE-based drug-disease-gene network 
Background
A huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among different biological entities in the context of personalized medicine and translational research. Recently, network-based computational approaches have gained popularity in investigating such heterogeneous data, proposing novel therapeutic targets and deciphering disease mechanisms. However, little effort has been devoted to investigating associations among drugs, diseases, and genes in an integrative manner.
Results
We propose a novel network-based computational framework to identify statistically over-expressed subnetwork patterns, called network motifs, in an integrated disease-drug-gene network extracted from Semantic MEDLINE. The framework consists of two steps. The first step is to construct an association network by extracting pair-wise associations between diseases, drugs and genes in Semantic MEDLINE using a domain pattern driven strategy. A Resource Description Framework (RDF)-linked data approach is used to re-organize the data to increase the flexibility of data integration, the interoperability within domain ontologies, and the efficiency of data storage. Unique associations among drugs, diseases, and genes are extracted for downstream network-based analysis. The second step is to apply a network-based approach to mine the local network structure of this heterogeneous network. Significant network motifs are then identified as the backbone of the network. A simplified network based on those significant motifs is then constructed to facilitate discovery. We implemented our computational framework and identified five network motifs, each of which corresponds to specific biological meanings. Three case studies demonstrate that novel associations are derived from the network topology analysis of reconstructed networks of significant network motifs, further validated by expert knowledge and functional enrichment analyses.
Conclusions
We have developed a novel network-based computational approach to investigate the heterogeneous drug-gene-disease network extracted from Semantic MEDLINE. We demonstrate the power of this approach by prioritizing candidate disease genes, inferring potential disease relationships, and proposing novel drug targets, within the context of the entire knowledge. The results indicate that such approach will facilitate the formulization of novel research hypotheses, which is critical for translational medicine research and personalized medicine.
doi:10.1186/2041-1480-5-33
PMCID: PMC4137727  PMID: 25170419
21.  HPD: an online integrated human pathway database enabling systems biology studies 
BMC Bioinformatics  2009;10(Suppl 11):S5.
Background
Pathway-oriented experimental and computational studies have led to a significant accumulation of biological knowledge concerning three major types of biological pathway events: molecular signaling events, gene regulation events, and metabolic reaction events. A pathway consists of a series of molecular pathway events that link molecular entities such as proteins, genes, and metabolites. There are approximately 300 biological pathway resources as of April 2009 according to the Pathguide database; however, these pathway databases generally have poor coverage or poor quality, and are difficult to integrate, due to syntactic-level and semantic-level data incompatibilities.
Results
We developed the Human Pathway Database (HPD) by integrating heterogeneous human pathway data that are either curated at the NCI Pathway Interaction Database (PID), Reactome, BioCarta, KEGG or indexed from the Protein Lounge Web sites. Integration of pathway data at syntactic, semantic, and schematic levels was based on a unified pathway data model and data warehousing-based integration techniques. HPD provides a comprehensive online view that connects human proteins, genes, RNA transcripts, enzymes, signaling events, metabolic reaction events, and gene regulatory events. At the time of this writing HPD includes 999 human pathways and more than 59,341 human molecular entities. The HPD software provides both a user-friendly Web interface for online use and a robust relational database backend for advanced pathway querying. This pathway tool enables users to 1) search for human pathways from different resources by simply entering genes/proteins involved in pathways or words appearing in pathway names, 2) analyze pathway-protein association, 3) study pathway-pathway similarity, and 4) build integrated pathway networks. We demonstrated the usage and characteristics of the new HPD through three breast cancer case studies.
Conclusion
HPD http://bio.informatics.iupui.edu/HPD is a new resource for searching, managing, and studying human biological pathways. Users of HPD can search against large collections of human biological pathways, compare related pathways and their molecular entity compositions, and build high-quality, expanded-scope disease pathway models. The current HPD software can help users address a wide range of pathway-related questions in human disease biology studies.
doi:10.1186/1471-2105-10-S11-S5
PMCID: PMC3226194  PMID: 19811689
22.  Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts 
PLoS Computational Biology  2009;5(7):e1000450.
The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin.
Author Summary
Molecular connectivity maps between drugs and a wide range of bio-molecular entities can help researchers to study and compare the molecular therapeutic/toxicological profiles of many candidate drugs. Recent studies in this area have focused on linking drug molecules and genes in specific disease contexts using drug-perturbed gene expression experiments, which can be costly and time-consuming to derive. In this paper, we developed a computational framework to build disease-specific drug-protein connectivity maps, by mining molecular interaction networks and PubMed abstracts. Using Alzheimer's Disease (AD) as a case study, we described how drug-protein molecular connectivity maps can be constructed to overcome data coverage and noise issues inherent in automatically extracted results. We showed that this new approach outperformed both curated drug target databases and conventional text mining systems in retrieving disease-related drugs, with an overall balanced performance of sensitivity, specificity, and positive predictive values. The AD molecular connectivity map contained novel information on AD-related genes/proteins, AD candidate drugs, and protein therapeutic/toxicological profiles of all the AD candidate drugs. Bi-clustering of the molecular connectivity map revealed interesting patterns of functionally similar proteins and drugs, therefore creating new opportunities for future drug development applications.
doi:10.1371/journal.pcbi.1000450
PMCID: PMC2709445  PMID: 19649302
23.  Multi-dimensional discovery of biomarker and phenotype complexes 
BMC Bioinformatics  2010;11(Suppl 9):S3.
Background
Given the rapid growth of translational research and personalized healthcare paradigms, the ability to relate and reason upon networks of bio-molecular and phenotypic variables at various levels of granularity in order to diagnose, stage and plan treatments for disease states is highly desirable. Numerous techniques exist that can be used to develop networks of co-expressed or otherwise related genes and clinical features. Such techniques can also be used to create formalized knowledge collections based upon the information incumbent to ontologies and domain literature. However, reports of integrative approaches that bridge such networks to create systems-level models of disease or wellness are notably lacking in the contemporary literature.
Results
In response to the preceding gap in knowledge and practice, we report upon a prototypical series of experiments that utilize multi-modal approaches to network induction. These experiments are intended to elicit meaningful and significant biomarker-phenotype complexes spanning multiple levels of granularity. This work has been performed in the experimental context of a large-scale clinical and basic science data repository maintained by the National Cancer Institute (NCI) funded Chronic Lymphocytic Leukemia Research Consortium.
Conclusions
Our results indicate that it is computationally tractable to link orthogonal networks of genes, clinical features, and conceptual knowledge to create multi-dimensional models of interrelated biomarkers and phenotypes. Further, our results indicate that such systems-level models contain interrelated bio-molecular and clinical markers capable of supporting hypothesis discovery and testing. Based on such findings, we propose a conceptual model intended to inform the cross-linkage of the results of such methods. This model has as its aim the identification of novel and knowledge-anchored biomarker-phenotype complexes.
doi:10.1186/1471-2105-11-S9-S3
PMCID: PMC2967744  PMID: 21044361
24.  Computable visually observed phenotype ontological framework for plants 
BMC Bioinformatics  2011;12:260.
Background
The ability to search for and precisely compare similar phenotypic appearances within and across species has vast potential in plant science and genetic research. The difficulty in doing so lies in the fact that many visual phenotypic data, especially visually observed phenotypes that often times cannot be directly measured quantitatively, are in the form of text annotations, and these descriptions are plagued by semantic ambiguity, heterogeneity, and low granularity. Though several bio-ontologies have been developed to standardize phenotypic (and genotypic) information and permit comparisons across species, these semantic issues persist and prevent precise analysis and retrieval of information. A framework suitable for the modeling and analysis of precise computable representations of such phenotypic appearances is needed.
Results
We have developed a new framework called the Computable Visually Observed Phenotype Ontological Framework for plants. This work provides a novel quantitative view of descriptions of plant phenotypes that leverages existing bio-ontologies and utilizes a computational approach to capture and represent domain knowledge in a machine-interpretable form. This is accomplished by means of a robust and accurate semantic mapping module that automatically maps high-level semantics to low-level measurements computed from phenotype imagery. The framework was applied to two different plant species with semantic rules mined and an ontology constructed. Rule quality was evaluated and showed high quality rules for most semantics. This framework also facilitates automatic annotation of phenotype images and can be adopted by different plant communities to aid in their research.
Conclusions
The Computable Visually Observed Phenotype Ontological Framework for plants has been developed for more efficient and accurate management of visually observed phenotypes, which play a significant role in plant genomics research. The uniqueness of this framework is its ability to bridge the knowledge of informaticians and plant science researchers by translating descriptions of visually observed phenotypes into standardized, machine-understandable representations, thus enabling the development of advanced information retrieval and phenotype annotation analysis tools for the plant science community.
doi:10.1186/1471-2105-12-260
PMCID: PMC3149582  PMID: 21702966
25.  Extracting semantically enriched events from biomedical literature 
BMC Bioinformatics  2012;13:108.
Background
Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them.
Results
Based on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP’09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP’09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task.
Conclusions
We have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare.
doi:10.1186/1471-2105-13-108
PMCID: PMC3464657  PMID: 22621266

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