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1.  SMART Platforms: Building the App Store for Biosurveillance 
To enable public health departments to develop “apps” to run on electronic health records (EHRs) for (1) biosurveillance and case reporting and (2) delivering alerts to the point of care. We describe a novel health information technology platform with substitutable apps constructed around core services enabling EHRs to function as iPhone-like platforms.
Health care information is a fundamental source of data for biosurveillance, yet configuring EHRs to report relevant data to health departments is technically challenging, labor intensive, and often requires custom solutions for each installation. Public health agencies wishing to deliver alerts to clinicians also must engage in an endless array of one-off systems integrations.
Despite a $48B investment in HIT, and meaningful use criteria requiring reporting to biosurveillance systems, most vendor electronic health records are architected monolithically, making modification difficult for hospitals and physician practices. An alternative approach is to reimagine EHRs as iPhone-like platforms supporting substitutable apps-based functionality. Substitutability is the capability inherent in a system of replacing one application with another of similar functionality.
Substitutability requires that the purchaser of an app can replace one application with another without being technically expert, without requiring re-engineering other applications that they are using, and without having to consult or require assistance of any of the vendors of previously installed or currently installed applications. Apps necessarily compete with each other promoting progress and adaptability.
The Substitutable Medical Applications, Reusable Technologies (SMART) Platforms project is funded by a $15M grant from Office of the National Coordinator of Health Information Technology’s Strategic Health IT Advanced Research Projects (SHARP) Program. All SMART standards are open and the core software is open source.
The SMART project promotes substitutability through an application programming interface (API) that can be adopted as part of a “container” built around by a wide variety of HIT, providing readonly access to the underlying data model and a software development toolkit to readily create apps. SMART containers are HIT systems, that have implemented the SMART API or a portion of it. Containers marshal data sources and present them consistently across the SMART API. SMART applications consume the API and are substitutable.
SMART provides a common platform supporting an “app store for biosurveillance” as an approach to enabling one stop shopping for public health departments—to create an app once, and distribute it everywhere.
Further, such apps can be readily updated or created—for example, in the case of an emerging infection, an app may be designed to collect additional data at emergency department triage. Or a public health department may widely distribute an app, interoperable with any SMART-enabled EMR, that delivers contextualized alerts when patient electronic records are opened, or through background processes.
SMART has sparked an ecosystem of apps developers and attracted existing health information technology platforms to adopt the SMART API—including, traditional, open source, and next generation EHRs, patient-facing platforms and health information exchanges. SMART-enabled platforms to date include the Cerner EMR, the WorldVista EHR, the OpenMRS EHR, the i2b2 analytic platform, and the Indivo X personal health record. The SMART team is working with the Mirth Corporation, to SMART-enable the HealthBridge and Redwood MedNet Health Information Exchanges. We have demonstrated that a single SMART app can run, unmodified, in all of these environments, as long as the underlying platform collects the required data types. Major EHR vendors are currently adapting the SMART API for their products.
The SMART system enables nimble customization of any electronic health record system to create either a reporting function (outgoing communication) or an alerting function (incoming communication) establishing a technology for a robust linkage between public health and clinical environments.
PMCID: PMC3692876
Electronic health records; Biosurveillance; Informatics; Application Programming Interfaces
2.  Semantic interoperability – Role and operationalization of the International Classification of Functioning, Disability and Health (ICF) 
Globalization and the advances in modern information and communication technologies (ICT) are changing the practice of health care and policy making. In the globalized economies of the 21 century, health systems will have to respond to the need of increasingly mobile citizens, patients and providers. At the same time the increased use of ICT is enabling health systems to systematize, process and integrate multiple data silos from different settings and at various levels. To meet these challenges effectively, the creation of an interoperable, global e-Health information infrastructure is critical. Data interoperability within and across heterogeneous health systems, however, is often hampered by differences in terminological inconsistencies and the lack of a common language, particularly when multiple communities of practice from different countries are involved.
Discuss the functionality and ontological requirements for ICF in achieving semantic interoperability of e-Health information systems.
Most solution attempts for interoperability to date have only focused on technical exchange of data in common formats. Automated health information exchange and aggregation is a very complex task which depends on many crucial prerequisites. The overall architecture of the health information system has to be defined clearly at macro and micro levels in terms of its building blocks and their characteristics. The taxonomic and conceptual features of the ICF make it an important architectural element in the overall design of e-Health information systems. To use the ICF in a digital environment the classification needs to be formalized and modeled using ontological principles and description logic. Ontological modeling is also required for linking assessment instruments and clinical terminologies (e.g. SNOMED) to the ICF.
To achieve semantic interoperability of e-Health systems a carefully elaborated overall health information system architecture has to be established. As a content standard, the ICF can play a pivotal role for meaningful and automated compilation and exchange of health information across sectors and levels. In order to fulfill this role a ICF ontology needs to be developed.
PMCID: PMC2707550
semantic interoperability; health and disability classification; ontology development
3.  Comprehensive effective and efficient global public health surveillance 
BMC Public Health  2010;10(Suppl 1):S3.
At a crossroads, global public health surveillance exists in a fragmented state. Slow to detect, register, confirm, and analyze cases of public health significance, provide feedback, and communicate timely and useful information to stakeholders, global surveillance is neither maximally effective nor optimally efficient. Stakeholders lack a globa surveillance consensus policy and strategy; officials face inadequate training and scarce resources.
Three movements now set the stage for transformation of surveillance: 1) adoption by Member States of the World Health Organization (WHO) of the revised International Health Regulations (IHR[2005]); 2) maturation of information sciences and the penetration of information technologies to distal parts of the globe; and 3) consensus that the security and public health communities have overlapping interests and a mutual benefit in supporting public health functions. For these to enhance surveillance competencies, eight prerequisites should be in place: politics, policies, priorities, perspectives, procedures, practices, preparation, and payers.
To achieve comprehensive, global surveillance, disparities in technical, logistic, governance, and financial capacities must be addressed. Challenges to closing these gaps include the lack of trust and transparency; perceived benefit at various levels; global governance to address data power and control; and specified financial support from globa partners.
We propose an end-state perspective for comprehensive, effective and efficient global, multiple-hazard public health surveillance and describe a way forward to achieve it. This end-state is universal, global access to interoperable public health information when it’s needed, where it’s needed. This vision mitigates the tension between two fundamental human rights: first, the right to privacy, confidentiality, and security of personal health information combined with the right of sovereign, national entities to the ownership and stewardship of public health information; and second, the right of individuals to access real-time public health information that might impact their lives.
The vision can be accomplished through an interoperable, global public health grid. Adopting guiding principles, the global community should circumscribe the overlapping interest, shared vision, and mutual benefit between the security and public health communities and define the boundaries. A global forum needs to be established to guide the consensus governance required for public health information sharing in the 21st century.
PMCID: PMC3005575  PMID: 21143825
4.  Advancing translational research with the Semantic Web 
BMC Bioinformatics  2007;8(Suppl 3):S2.
A fundamental goal of the U.S. National Institute of Health (NIH) "Roadmap" is to strengthen Translational Research, defined as the movement of discoveries in basic research to application at the clinical level. A significant barrier to translational research is the lack of uniformly structured data across related biomedical domains. The Semantic Web is an extension of the current Web that enables navigation and meaningful use of digital resources by automatic processes. It is based on common formats that support aggregation and integration of data drawn from diverse sources. A variety of technologies have been built on this foundation that, together, support identifying, representing, and reasoning across a wide range of biomedical data. The Semantic Web Health Care and Life Sciences Interest Group (HCLSIG), set up within the framework of the World Wide Web Consortium, was launched to explore the application of these technologies in a variety of areas. Subgroups focus on making biomedical data available in RDF, working with biomedical ontologies, prototyping clinical decision support systems, working on drug safety and efficacy communication, and supporting disease researchers navigating and annotating the large amount of potentially relevant literature.
We present a scenario that shows the value of the information environment the Semantic Web can support for aiding neuroscience researchers. We then report on several projects by members of the HCLSIG, in the process illustrating the range of Semantic Web technologies that have applications in areas of biomedicine.
Semantic Web technologies present both promise and challenges. Current tools and standards are already adequate to implement components of the bench-to-bedside vision. On the other hand, these technologies are young. Gaps in standards and implementations still exist and adoption is limited by typical problems with early technology, such as the need for a critical mass of practitioners and installed base, and growing pains as the technology is scaled up. Still, the potential of interoperable knowledge sources for biomedicine, at the scale of the World Wide Web, merits continued work.
PMCID: PMC1892099  PMID: 17493285
5.  Lessons learned in detailed clinical modeling at Intermountain Healthcare 
Background and objective
Intermountain Healthcare has a long history of using coded terminology and detailed clinical models (DCMs) to govern storage of clinical data to facilitate decision support and semantic interoperability. The latest iteration of DCMs at Intermountain is called the clinical element model (CEM). We describe the lessons learned from our CEM efforts with regard to subjective decisions a modeler frequently needs to make in creating a CEM. We present insights and guidelines, but also describe situations in which use cases conflict with the guidelines. We propose strategies that can help reconcile the conflicts. The hope is that these lessons will be helpful to others who are developing and maintaining DCMs in order to promote sharing and interoperability.
We have used the Clinical Element Modeling Language (CEML) to author approximately 5000 CEMs.
Based on our experience, we have formulated guidelines to lead our modelers through the subjective decisions they need to make when authoring models. Reported here are guidelines regarding precoordination/postcoordination, dividing content between the model and the terminology, modeling logical attributes, and creating iso-semantic models. We place our lessons in context, exploring the potential benefits of an implementation layer, an iso-semantic modeling framework, and ontologic technologies.
We assert that detailed clinical models can advance interoperability and sharing, and that our guidelines, an implementation layer, and an iso-semantic framework will support our progress toward that goal.
PMCID: PMC4215059  PMID: 24993546
Medical Records Systems, Computerized/Standards; Semantics; Health Information Systems/Standards; Information Storage and Retrieval; Vocabulary, Controlled
6.  Computational toxicology using the OpenTox application programming interface and Bioclipse 
BMC Research Notes  2011;4:487.
Toxicity is a complex phenomenon involving the potential adverse effect on a range of biological functions. Predicting toxicity involves using a combination of experimental data (endpoints) and computational methods to generate a set of predictive models. Such models rely strongly on being able to integrate information from many sources. The required integration of biological and chemical information sources requires, however, a common language to express our knowledge ontologically, and interoperating services to build reliable predictive toxicology applications.
This article describes progress in extending the integrative bio- and cheminformatics platform Bioclipse to interoperate with OpenTox, a semantic web framework which supports open data exchange and toxicology model building. The Bioclipse workbench environment enables functionality from OpenTox web services and easy access to OpenTox resources for evaluating toxicity properties of query molecules. Relevant cases and interfaces based on ten neurotoxins are described to demonstrate the capabilities provided to the user. The integration takes advantage of semantic web technologies, thereby providing an open and simplifying communication standard. Additionally, the use of ontologies ensures proper interoperation and reliable integration of toxicity information from both experimental and computational sources.
A novel computational toxicity assessment platform was generated from integration of two open science platforms related to toxicology: Bioclipse, that combines a rich scriptable and graphical workbench environment for integration of diverse sets of information sources, and OpenTox, a platform for interoperable toxicology data and computational services. The combination provides improved reliability and operability for handling large data sets by the use of the Open Standards from the OpenTox Application Programming Interface. This enables simultaneous access to a variety of distributed predictive toxicology databases, and algorithm and model resources, taking advantage of the Bioclipse workbench handling the technical layers.
PMCID: PMC3264531  PMID: 22075173
7.  Assessment of Collaboration and Interoperability in an Information Management System to Support Bioscience Research 
Biomedical researchers often have to work on massive, detailed, and heterogeneous datasets that raise new challenges of information management. This study reports an investigation into the nature of the problems faced by the researchers in two bioscience test laboratories when dealing with their data management applications. Data were collected using ethnographic observations, questionnaires, and semi-structured interviews. The major problems identified in working with these systems were related to data organization, publications, and collaboration. The interoperability standards were analyzed using a C4I framework at the level of connection, communication, consolidation, and collaboration. Such an analysis was found to be useful in judging the capabilities of data management systems at different levels of technological competency. While collaboration and system interoperability are the “must have” attributes of these biomedical scientific laboratory information management applications, usability and human interoperability are the other design concerns that must also be addressed for easy use and implementation.
PMCID: PMC2815423  PMID: 20351900
8.  Open data models for smart health interconnected applications: the example of openEHR 
Smart Health is known as a concept that enhances networking, intelligent data processing and combining patient data with other parameters. Open data models can play an important role in creating a framework for providing interoperable data services that support the development of innovative Smart Health applications profiting from data fusion and sharing.
This article describes a model-driven engineering approach based on standardized clinical information models and explores its application for the development of interoperable electronic health record systems. The following possible model-driven procedures were considered: provision of data schemes for data exchange, automated generation of artefacts for application development and native platforms that directly execute the models. The applicability of the approach in practice was examined using the openEHR framework as an example.
A comprehensive infrastructure for model-driven engineering of electronic health records is presented using the example of the openEHR framework. It is shown that data schema definitions to be used in common practice software development processes can be derived from domain models. The capabilities for automatic creation of implementation artefacts (e.g., data entry forms) are demonstrated. Complementary programming libraries and frameworks that foster the use of open data models are introduced. Several compatible health data platforms are listed. They provide standard based interfaces for interconnecting with further applications.
Open data models help build a framework for interoperable data services that support the development of innovative Smart Health applications. Related tools for model-driven application development foster semantic interoperability and interconnected innovative applications.
PMCID: PMC5075152  PMID: 27770769
Clinical information models; Electronic health record; Model-driven engineering; Open data models; openEHR; Semantic interoperability
9.  Collaborative development of predictive toxicology applications 
OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.
The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.
Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way.
PMCID: PMC2941473  PMID: 20807436
10.  A knowledge-based taxonomy of critical factors for adopting electronic health record systems by physicians: a systematic literature review 
The health care sector is an area of social and economic interest in several countries; therefore, there have been lots of efforts in the use of electronic health records. Nevertheless, there is evidence suggesting that these systems have not been adopted as it was expected, and although there are some proposals to support their adoption, the proposed support is not by means of information and communication technology which can provide automatic tools of support. The aim of this study is to identify the critical adoption factors for electronic health records by physicians and to use them as a guide to support their adoption process automatically.
This paper presents, based on the PRISMA statement, a systematic literature review in electronic databases with adoption studies of electronic health records published in English. Software applications that manage and process the data in the electronic health record have been considered, i.e.: computerized physician prescription, electronic medical records, and electronic capture of clinical data. Our review was conducted with the purpose of obtaining a taxonomy of the physicians main barriers for adopting electronic health records, that can be addressed by means of information and communication technology; in particular with the information technology roles of the knowledge management processes. Which take us to the question that we want to address in this work: "What are the critical adoption factors of electronic health records that can be supported by information and communication technology?". Reports from eight databases covering electronic health records adoption studies in the medical domain, in particular those focused on physicians, were analyzed.
The review identifies two main issues: 1) a knowledge-based classification of critical factors for adopting electronic health records by physicians; and 2) the definition of a base for the design of a conceptual framework for supporting the design of knowledge-based systems, to assist the adoption process of electronic health records in an automatic fashion. From our review, six critical adoption factors have been identified: user attitude towards information systems, workflow impact, interoperability, technical support, communication among users, and expert support. The main limitation of the taxonomy is the different impact of the adoption factors of electronic health records reported by some studies depending on the type of practice, setting, or attention level; however, these features are a determinant aspect with regard to the adoption rate for the latter rather than the presence of a specific critical adoption factor.
The critical adoption factors established here provide a sound theoretical basis for research to understand, support, and facilitate the adoption of electronic health records to physicians in benefit of patients.
PMCID: PMC2970582  PMID: 20950458
11.  linkedISA: semantic representation of ISA-Tab experimental metadata 
BMC Bioinformatics  2014;15(Suppl 14):S4.
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.
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.
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.
PMCID: PMC4255742  PMID: 25472428
12.  The Quixote project: Collaborative and Open Quantum Chemistry data management in the Internet age 
Computational Quantum Chemistry has developed into a powerful, efficient, reliable and increasingly routine tool for exploring the structure and properties of small to medium sized molecules. Many thousands of calculations are performed every day, some offering results which approach experimental accuracy. However, in contrast to other disciplines, such as crystallography, or bioinformatics, where standard formats and well-known, unified databases exist, this QC data is generally destined to remain locally held in files which are not designed to be machine-readable. Only a very small subset of these results will become accessible to the wider community through publication.
In this paper we describe how the Quixote Project is developing the infrastructure required to convert output from a number of different molecular quantum chemistry packages to a common semantically rich, machine-readable format and to build respositories of QC results. Such an infrastructure offers benefits at many levels. The standardised representation of the results will facilitate software interoperability, for example making it easier for analysis tools to take data from different QC packages, and will also help with archival and deposition of results. The repository infrastructure, which is lightweight and built using Open software components, can be implemented at individual researcher, project, organisation or community level, offering the exciting possibility that in future many of these QC results can be made publically available, to be searched and interpreted just as crystallography and bioinformatics results are today.
Although we believe that quantum chemists will appreciate the contribution the Quixote infrastructure can make to the organisation and and exchange of their results, we anticipate that greater rewards will come from enabling their results to be consumed by a wider community. As the respositories grow they will become a valuable source of chemical data for use by other disciplines in both research and education.
The Quixote project is unconventional in that the infrastructure is being implemented in advance of a full definition of the data model which will eventually underpin it. We believe that a working system which offers real value to researchers based on tools and shared, searchable repositories will encourage early participation from a broader community, including both producers and consumers of data. In the early stages, searching and indexing can be performed on the chemical subject of the calculations, and well defined calculation meta-data. The process of defining more specific quantum chemical definitions, adding them to dictionaries and extracting them consistently from the results of the various software packages can then proceed in an incremental manner, adding additional value at each stage.
Not only will these results help to change the data management model in the field of Quantum Chemistry, but the methodology can be applied to other pressing problems related to data in computational and experimental science.
PMCID: PMC3206452  PMID: 21999363
13.  S&I Public Health Reporting Initiative: Improving Standardization of Surveillance 
The objective of this panel is to inform the ISDS community of the progress made in the Standards & Interoperability (S&I) Framework Public Health Reporting Initiative (PHRI). Also, it will provide some context of how the initiative will likely affect biosurveillance reporting in Meaningful Use Stage 3 and future harmonization of data standards requirements for public health reporting.
The S&I Framework is an Office of National Coordinator (ONC) initiative designed to support individual working groups who focus on a specific interoperability challenge. One of these working groups within the S&I Framework is the PHRI, which is using the S&I Framework as a platform for a community-led project focused on simplifying public health reporting and ensuring EHR interoperability with public health information systems. PHRI hopes to create a new public health reporting objective for Meaningful Use Stage 3 that is broader than the current program-specific objectives and will lay the ground work for all public health reporting in the future. To date, the initiative received over 30 descriptions of different types of public health reporting that were then grouped into 5 domain categories. Each domain category was decomposed into component elements and commonalities were identified. The PHRI is now working to reconstruct a single model of public health reporting through a consensus process that will soon lead to a pilot demonstration of the most ready reporting types. This panel will outline progress, challenges, and next steps of the initiative as well as describe how the initiative may affect a standard language for biosurveillance reporting.
Michael Coletta will provide an introduction and background of the S&I PHRI. He will describe how the PHRI intends to impact reporting in a way that is universal and helpful to both HIT vendors and public health programs.
Nikolay Lipskiy will provide an understanding of the ground breaking nature of collaboration and harmonization that is occurring across public health programs. He will describe the data harmonization process, outcomes, and hopes for the future of this work.
David Birnbaum has been a very active member of PHRI and has consistently advocated for the inclusion of Healthcare Associated Infections (HAI) reporting in Meaningful Use as a model. David has been representing one of the largest user communities among those farthest along toward automated uploading of data to public health agencies. He will describe the opportunities and challenges of this initiative from the perspective of a participant representing an already highly evolved reporting system (CDC’s National Healthcare Safety Network system).
John Abellera has been the steward of the communicable disease reporting user story for the PHRI. He will describe the current challenges to reporting and how the PHRI proposed changes could improve communicable disease reporting efforts.
This will be followed by an open discussion with the audience intended to elicit reactions regarding an eventual consolidation from individual report specific specification documents to one core report specification across public health reporting programs which is then supplemented with both program specific specifications and a limited number of implementation specific specifications.
Plan to engage audience: Have a prepared list of questions to pose to the audience for reactions and discussion (to be supplied if participation is low).
PMCID: PMC3692744
Standards; Interoperability; Meaningful Use; Reporting; Stage 3
14.  Systems Medicine 2.0: Potential Benefits of Combining Electronic Health Care Records With Systems Science Models 
The global burden of disease is increasingly dominated by non-communicable diseases.These diseases are less amenable to curative and preventative interventions than communicable disease. This presents a challenge to medical practice and medical research, both of which are experiencing diminishing returns from increasing investment.
Our aim was to (1) review how medical knowledge is generated, and its limitations, (2) assess the potential for emerging technologies and ideas to improve medical research, and (3) suggest solutions and recommendations to increase medical research efficiency on non-communicable diseases.
We undertook an unsystematic review of peer-reviewed literature and technology websites.
Our review generated the following conclusions and recommendations. (1) Medical knowledge continues to be generated in a reductionist paradigm. This oversimplifies our models of disease, rendering them ineffective to sufficiently understand the complex nature of non-communicable diseases. (2) Some of these failings may be overcome by adopting a “Systems Medicine” paradigm, where the human body is modeled as a complex adaptive system. That is, a system with multiple components and levels interacting in complex ways, wherein disease emerges from slow changes to the system set-up. Pursuing systems medicine research will require larger datasets. (3) Increased data sharing between researchers, patients, and clinicians could provide this unmet need for data. The recent emergence of electronic health care records (EHR) could potentially facilitate this in real-time and at a global level. (4) Efforts should continue to aggregate anonymous EHR data into large interoperable data silos and release this to researchers. However, international collaboration, data linkage, and obtaining additional information from patients will remain challenging. (5) Efforts should also continue towards “Medicine 2.0”. Patients should be given access to their personal EHR data. Subsequently, online communities can give researchers the opportunity to ask patients for direct access to the patient’s EHR data and request additional study-specific information. However, selection bias towards patients who use Web 2.0 technology may be difficult to overcome.
Systems medicine, when combined with large-scale data sharing, has the potential to raise our understanding of non-communicable diseases, foster personalized medicine, and make substantial progress towards halting, curing, and preventing non-communicable diseases. Large-scale data amalgamation remains a core challenge and needs to be supported. A synthesis of “Medicine 2.0” and “Systems Science” concepts into “Systems Medicine 2.0” could take decades to materialize but holds much promise.
PMCID: PMC4387294  PMID: 25831125
gene-environment interaction; systems theory; electronic health records; epidemiology; online social networks; crowd-sourcing; Web 2.0
15.  Toward a roadmap in global biobanking for health 
European Journal of Human Genetics  2012;20(11):1105-1111.
Biobanks can have a pivotal role in elucidating disease etiology, translation, and advancing public health. However, meeting these challenges hinges on a critical shift in the way science is conducted and requires biobank harmonization. There is growing recognition that a common strategy is imperative to develop biobanking globally and effectively. To help guide this strategy, we articulate key principles, goals, and priorities underpinning a roadmap for global biobanking to accelerate health science, patient care, and public health. The need to manage and share very large amounts of data has driven innovations on many fronts. Although technological solutions are allowing biobanks to reach new levels of integration, increasingly powerful data-collection tools, analytical techniques, and the results they generate raise new ethical and legal issues and challenges, necessitating a reconsideration of previous policies, practices, and ethical norms. These manifold advances and the investments that support them are also fueling opportunities for biobanks to ultimately become integral parts of health-care systems in many countries. International harmonization to increase interoperability and sustainability are two strategic priorities for biobanking. Tackling these issues requires an environment favorably inclined toward scientific funding and equipped to address socio-ethical challenges. Cooperation and collaboration must extend beyond systems to enable the exchange of data and samples to strategic alliances between many organizations, including governmental bodies, funding agencies, public and private science enterprises, and other stakeholders, including patients. A common vision is required and we articulate the essential basis of such a vision herein.
PMCID: PMC3477856  PMID: 22713808
16.  A patient-centric distribution architecture for medical image sharing 
Over the past decade, rapid development of imaging technologies has resulted in the introduction of improved imaging devices, such as multi-modality scanners that produce combined positron emission tomography-computed tomography (PET-CT) images. The adoption of picture archiving and communication systems (PACS) in hospitals have dramatically improved the ability to digitally share medical image studies via portable storage, mobile devices and the Internet. This has in turn led to increased productivity, greater flexibility, and improved communication between hospital staff, referring physicians, and outpatients. However, many of these sharing and viewing capabilities are limited to proprietary vendor-specific applications. Furthermore, there are still interoperability and deployment issues which reduce the rate of adoption of such technologies, thus leaving many stakeholders, particularly outpatients and referring physicians, with access to only traditional still images with no ability to view or interpret the data in full. In this paper, we present a distribution architecture for medical image display across numerous devices and media, which uses a preprocessor and an in-built networking framework to improve compatibility and promote greater accessibility of medical data. Our INVOLVE2 system consists of three main software modules: 1) a preprocessor, which collates and converts imaging studies into a compressed and distributable format; 2) a PACS-compatible workflow for self-managing distribution of medical data, e.g. via CD USB, network etc; 3) support for potential mobile and web-based data access. The focus of this study was on cultivating patient-centric care, by allowing outpatient users to comfortably access and interpret their own data. As such, the image viewing software included on our cross-platform CDs was designed with a simple and intuitive user-interface (UI) for use by outpatients and referring physicians. Furthermore, digital image access via mobile devices or web-based access enables users to engage with their data in a convenient and user-friendly way. We evaluated the INVOLVE2 system using a pilot deployment in a hospital environment.
PMCID: PMC4336110  PMID: 25825655
Telemedicine; Medical image viewer; patient-centric healthcare; Clinical workflow support system; Positron emission tomography - computed tomography
17.  Human genome meeting 2016 
Srivastava, A. K. | Wang, Y. | Huang, R. | Skinner, C. | Thompson, T. | Pollard, L. | Wood, T. | Luo, F. | Stevenson, R. | Polimanti, R. | Gelernter, J. | Lin, X. | Lim, I. Y. | Wu, Y. | Teh, A. L. | Chen, L. | Aris, I. M. | Soh, S. E. | Tint, M. T. | MacIsaac, J. L. | Yap, F. | Kwek, K. | Saw, S. M. | Kobor, M. S. | Meaney, M. J. | Godfrey, K. M. | Chong, Y. S. | Holbrook, J. D. | Lee, Y. S. | Gluckman, P. D. | Karnani, N. | Kapoor, A. | Lee, D. | Chakravarti, A. | Maercker, C. | Graf, F. | Boutros, M. | Stamoulis, G. | Santoni, F. | Makrythanasis, P. | Letourneau, A. | Guipponi, M. | Panousis, N. | Garieri, M. | Ribaux, P. | Falconnet, E. | Borel, C. | Antonarakis, S. E. | Kumar, S. | Curran, J. | Blangero, J. | Chatterjee, S. | Kapoor, A. | Akiyama, J. | Auer, D. | Berrios, C. | Pennacchio, L. | Chakravarti, A. | Donti, T. R. | Cappuccio, G. | Miller, M. | Atwal, P. | Kennedy, A. | Cardon, A. | Bacino, C. | Emrick, L. | Hertecant, J. | Baumer, F. | Porter, B. | Bainbridge, M. | Bonnen, P. | Graham, B. | Sutton, R. | Sun, Q. | Elsea, S. | Hu, Z. | Wang, P. | Zhu, Y. | Zhao, J. | Xiong, M. | Bennett, David A. | Hidalgo-Miranda, A. | Romero-Cordoba, S. | Rodriguez-Cuevas, S. | Rebollar-Vega, R. | Tagliabue, E. | Iorio, M. | D’Ippolito, E. | Baroni, S. | Kaczkowski, B. | Tanaka, Y. | Kawaji, H. | Sandelin, A. | Andersson, R. | Itoh, M. | Lassmann, T. | Hayashizaki, Y. | Carninci, P. | Forrest, A. R. R. | Semple, C. A. | Rosenthal, E. A. | Shirts, B. | Amendola, L. | Gallego, C. | Horike-Pyne, M. | Burt, A. | Robertson, P. | Beyers, P. | Nefcy, C. | Veenstra, D. | Hisama, F. | Bennett, R. | Dorschner, M. | Nickerson, D. | Smith, J. | Patterson, K. | Crosslin, D. | Nassir, R. | Zubair, N. | Harrison, T. | Peters, U. | Jarvik, G. | Menghi, F. | Inaki, K. | Woo, X. | Kumar, P. | Grzeda, K. | Malhotra, A. | Kim, H. | Ucar, D. | Shreckengast, P. | Karuturi, K. | Keck, J. | Chuang, J. | Liu, E. T. | Ji, B. | Tyler, A. | Ananda, G. | Carter, G. | Nikbakht, H. | Montagne, M. | Zeinieh, M. | Harutyunyan, A. | Mcconechy, M. | Jabado, N. | Lavigne, P. | Majewski, J. | Goldstein, J. B. | Overman, M. | Varadhachary, G. | Shroff, R. | Wolff, R. | Javle, M. | Futreal, A. | Fogelman, D. | Bravo, L. | Fajardo, W. | Gomez, H. | Castaneda, C. | Rolfo, C. | Pinto, J. A. | Akdemir, K. C. | Chin, L. | Futreal, A. | Patterson, S. | Statz, C. | Mockus, S. | Nikolaev, S. N. | Bonilla, X. I. | Parmentier, L. | King, B. | Bezrukov, F. | Kaya, G. | Zoete, V. | Seplyarskiy, V. | Sharpe, H. | McKee, T. | Letourneau, A. | Ribaux, P. | Popadin, K. | Basset-Seguin, N. | Chaabene, R. Ben | Santoni, F. | Andrianova, M. | Guipponi, M. | Garieri, M. | Verdan, C. | Grosdemange, K. | Sumara, O. | Eilers, M. | Aifantis, I. | Michielin, O. | de Sauvage, F. | Antonarakis, S. | Likhitrattanapisal, S. | Lincoln, S. | Kurian, A. | Desmond, A. | Yang, S. | Kobayashi, Y. | Ford, J. | Ellisen, L. | Peters, T. L. | Alvarez, K. R. | Hollingsworth, E. F. | Lopez-Terrada, D. H. | Hastie, A. | Dzakula, Z. | Pang, A. W. | Lam, E. T. | Anantharaman, T. | Saghbini, M. | Cao, H. | Gonzaga-Jauregui, C. | Ma, L. | King, A. | Rosenzweig, E. Berman | Krishnan, U. | Reid, J. G. | Overton, J. D. | Dewey, F. | Chung, W. K. | Small, K. | DeLuca, A. | Cremers, F. | Lewis, R. A. | Puech, V. | Bakall, B. | Silva-Garcia, R. | Rohrschneider, K. | Leys, M. | Shaya, F. S. | Stone, E. | Sobreira, N. L. | Schiettecatte, F. | Ling, H. | Pugh, E. | Witmer, D. | Hetrick, K. | Zhang, P. | Doheny, K. | Valle, D. | Hamosh, A. | Jhangiani, S. N. | Akdemir, Z. Coban | Bainbridge, M. N. | Charng, W. | Wiszniewski, W. | Gambin, T. | Karaca, E. | Bayram, Y. | Eldomery, M. K. | Posey, J. | Doddapaneni, H. | Hu, J. | Sutton, V. R. | Muzny, D. M. | Boerwinkle, E. A. | Valle, D. | Lupski, J. R. | Gibbs, R. A. | Shekar, S. | Salerno, W. | English, A. | Mangubat, A. | Bruestle, J. | Thorogood, A. | Knoppers, B. M. | Takahashi, H. | Nitta, K. R. | Kozhuharova, A. | Suzuki, A. M. | Sharma, H. | Cotella, D. | Santoro, C. | Zucchelli, S. | Gustincich, S. | Carninci, P. | Mulvihill, J. J. | Baynam, G. | Gahl, W. | Groft, S. C. | Kosaki, K. | Lasko, P. | Melegh, B. | Taruscio, D. | Ghosh, R. | Plon, S. | Scherer, S. | Qin, X. | Sanghvi, R. | Walker, K. | Chiang, T. | Muzny, D. | Wang, L. | Black, J. | Boerwinkle, E. | Weinshilboum, R. | Gibbs, R. | Karpinets, T. | Calderone, T. | Wani, K. | Yu, X. | Creasy, C. | Haymaker, C. | Forget, M. | Nanda, V. | Roszik, J. | Wargo, J. | Haydu, L. | Song, X. | Lazar, A. | Gershenwald, J. | Davies, M. | Bernatchez, C. | Zhang, J. | Futreal, A. | Woodman, S. | Chesler, E. J. | Reynolds, T. | Bubier, J. A. | Phillips, C. | Langston, M. A. | Baker, E. J. | Xiong, M. | Ma, L. | Lin, N. | Amos, C. | Lin, N. | Wang, P. | Zhu, Y. | Zhao, J. | Calhoun, V. | Xiong, M. | Dobretsberger, O. | Egger, M. | Leimgruber, F. | Sadedin, S. | Oshlack, A. | Antonio, V. A. A. | Ono, N. | Ahmed, Z. | Bolisetty, M. | Zeeshan, S. | Anguiano, E. | Ucar, D. | Sarkar, A. | Nandineni, M. R. | Zeng, C. | Shao, J. | Cao, H. | Hastie, A. | Pang, A. W. | Lam, E. T. | Liang, T. | Pham, K. | Saghbini, M. | Dzakula, Z. | Chee-Wei, Y. | Dongsheng, L. | Lai-Ping, W. | Lian, D. | Hee, R. O. Twee | Yunus, Y. | Aghakhanian, F. | Mokhtar, S. S. | Lok-Yung, C. V. | Bhak, J. | Phipps, M. | Shuhua, X. | Yik-Ying, T. | Kumar, V. | Boon-Peng, H. | Campbell, I. | Young, M. -A. | James, P. | Rain, M. | Mohammad, G. | Kukreti, R. | Pasha, Q. | Akilzhanova, A. R. | Guelly, C. | Abilova, Z. | Rakhimova, S. | Akhmetova, A. | Kairov, U. | Trajanoski, S. | Zhumadilov, Z. | Bekbossynova, M. | Schumacher, C. | Sandhu, S. | Harkins, T. | Makarov, V. | Doddapaneni, H. | Glenn, R. | Momin, Z. | Dilrukshi, B. | Chao, H. | Meng, Q. | Gudenkauf, B. | Kshitij, R. | Jayaseelan, J. | Nessner, C. | Lee, S. | Blankenberg, K. | Lewis, L. | Hu, J. | Han, Y. | Dinh, H. | Jireh, S. | Walker, K. | Boerwinkle, E. | Muzny, D. | Gibbs, R. | Hu, J. | Walker, K. | Buhay, C. | Liu, X. | Wang, Q. | Sanghvi, R. | Doddapaneni, H. | Ding, Y. | Veeraraghavan, N. | Yang, Y. | Boerwinkle, E. | Beaudet, A. L. | Eng, C. M. | Muzny, D. M. | Gibbs, R. A. | Worley, K. C. C. | Liu, Y. | Hughes, D. S. T. | Murali, S. C. | Harris, R. A. | English, A. C. | Qin, X. | Hampton, O. A. | Larsen, P. | Beck, C. | Han, Y. | Wang, M. | Doddapaneni, H. | Kovar, C. L. | Salerno, W. J. | Yoder, A. | Richards, S. | Rogers, J. | Lupski, J. R. | Muzny, D. M. | Gibbs, R. A. | Meng, Q. | Bainbridge, M. | Wang, M. | Doddapaneni, H. | Han, Y. | Muzny, D. | Gibbs, R. | Harris, R. A. | Raveenedran, M. | Xue, C. | Dahdouli, M. | Cox, L. | Fan, G. | Ferguson, B. | Hovarth, J. | Johnson, Z. | Kanthaswamy, S. | Kubisch, M. | Platt, M. | Smith, D. | Vallender, E. | Wiseman, R. | Liu, X. | Below, J. | Muzny, D. | Gibbs, R. | Yu, F. | Rogers, J. | Lin, J. | Zhang, Y. | Ouyang, Z. | Moore, A. | Wang, Z. | Hofmann, J. | Purdue, M. | Stolzenberg-Solomon, R. | Weinstein, S. | Albanes, D. | Liu, C. S. | Cheng, W. L. | Lin, T. T. | Lan, Q. | Rothman, N. | Berndt, S. | Chen, E. S. | Bahrami, H. | Khoshzaban, A. | Keshal, S. Heidari | Bahrami, H. | Khoshzaban, A. | Keshal, S. Heidari | Alharbi, K. K. R. | Zhalbinova, M. | Akilzhanova, A. | Rakhimova, S. | Bekbosynova, M. | Myrzakhmetova, S. | Matar, M. | Mili, N. | Molinari, R. | Ma, Y. | Guerrier, S. | Elhawary, N. | Tayeb, M. | Bogari, N. | Qotb, N. | McClymont, S. A. | Hook, P. W. | Goff, L. A. | McCallion, A. | Kong, Y. | Charette, J. R. | Hicks, W. L. | Naggert, J. K. | Zhao, L. | Nishina, P. M. | Edrees, B. M. | Athar, M. | Al-Allaf, F. A. | Taher, M. M. | Khan, W. | Bouazzaoui, A. | Harbi, N. A. | Safar, R. | Al-Edressi, H. | Anazi, A. | Altayeb, N. | Ahmed, M. A. | Alansary, K. | Abduljaleel, Z. | Kratz, A. | Beguin, P. | Poulain, S. | Kaneko, M. | Takahiko, C. | Matsunaga, A. | Kato, S. | Suzuki, A. M. | Bertin, N. | Lassmann, T. | Vigot, R. | Carninci, P. | Plessy, C. | Launey, T. | Graur, D. | Lee, D. | Kapoor, A. | Chakravarti, A. | Friis-Nielsen, J. | Izarzugaza, J. M. | Brunak, S. | Chakraborty, A. | Basak, J. | Mukhopadhyay, A. | Soibam, B. S. | Das, D. | Biswas, N. | Das, S. | Sarkar, S. | Maitra, A. | Panda, C. | Majumder, P. | Morsy, H. | Gaballah, A. | Samir, M. | Shamseya, M. | Mahrous, H. | Ghazal, A. | Arafat, W. | Hashish, M. | Gruber, J. J. | Jaeger, N. | Snyder, M. | Patel, K. | Bowman, S. | Davis, T. | Kraushaar, D. | Emerman, A. | Russello, S. | Henig, N. | Hendrickson, C. | Zhang, K. | Rodriguez-Dorantes, M. | Cruz-Hernandez, C. D. | Garcia-Tobilla, C. D. P. | Solorzano-Rosales, S. | Jäger, N. | Chen, J. | Haile, R. | Hitchins, M. | Brooks, J. D. | Snyder, M. | Jiménez-Morales, S. | Ramírez, M. | Nuñez, J. | Bekker, V. | Leal, Y. | Jiménez, E. | Medina, A. | Hidalgo, A. | Mejía, J. | Halytskiy, V. | Naggert, J. | Collin, G. B. | DeMauro, K. | Hanusek, R. | Nishina, P. M. | Belhassa, K. | Belhassan, K. | Bouguenouch, L. | Samri, I. | Sayel, H. | moufid, FZ. | El Bouchikhi, I. | Trhanint, S. | Hamdaoui, H. | Elotmani, I. | Khtiri, I. | Kettani, O. | Quibibo, L. | Ahagoud, M. | Abbassi, M. | Ouldim, K. | Marusin, A. V. | Kornetov, A. N. | Swarovskaya, M. | Vagaiceva, K. | Stepanov, V. | De La Paz, E. M. Cutiongco | Sy, R. | Nevado, J. | Reganit, P. | Santos, L. | Magno, J. D. | Punzalan, F. E. | Ona, D. | Llanes, E. | Santos-Cortes, R. L. | Tiongco, R. | Aherrera, J. | Abrahan, L. | Pagauitan-Alan, P. | Morelli, K. H. | Domire, J. S. | Pyne, N. | Harper, S. | Burgess, R. | Zhalbinova, M. | Akilzhanova, A. | Rakhimova, S. | Bekbosynova, M. | Myrzakhmetova, S. | Gari, M. A. | Dallol, A. | Alsehli, H. | Gari, A. | Gari, M. | Abuzenadah, A. | Thomas, M. | Sukhai, M. | Garg, S. | Misyura, M. | Zhang, T. | Schuh, A. | Stockley, T. | Kamel-Reid, S. | Sherry, S. | Xiao, C. | Slotta, D. | Rodarmer, K. | Feolo, M. | Kimelman, M. | Godynskiy, G. | O’Sullivan, C. | Yaschenko, E. | Xiao, C. | Yaschenko, E. | Sherry, S. | Rangel-Escareño, C. | Rueda-Zarate, H. | Tayubi, I. A. | Mohammed, R. | Ahmed, I. | Ahmed, T. | Seth, S. | Amin, S. | Song, X. | Mao, X. | Sun, H. | Verhaak, R. G. | Futreal, A. | Zhang, J. | Whiite, S. J. | Chiang, T. | English, A. | Farek, J. | Kahn, Z. | Salerno, W. | Veeraraghavan, N. | Boerwinkle, E. | Gibbs, R. | Kasukawa, T. | Lizio, M. | Harshbarger, J. | Hisashi, S. | Severin, J. | Imad, A. | Sahin, S. | Freeman, T. C. | Baillie, K. | Sandelin, A. | Carninci, P. | Forrest, A. R. R. | Kawaji, H. | Salerno, W. | English, A. | Shekar, S. N. | Mangubat, A. | Bruestle, J. | Boerwinkle, E. | Gibbs, R. A. | Salem, A. H. | Ali, M. | Ibrahim, A. | Ibrahim, M. | Barrera, H. A. | Garza, L. | Torres, J. A. | Barajas, V. | Ulloa-Aguirre, A. | Kershenobich, D. | Mortaji, Shahroj | Guizar, Pedro | Loera, Eliezer | Moreno, Karen | De León, Adriana | Monsiváis, Daniela | Gómez, Jackeline | Cardiel, Raquel | Fernandez-Lopez, J. C. | Bonifaz-Peña, V. | Rangel-Escareño, C. | Hidalgo-Miranda, A. | Contreras, A. V. | Polfus, L. | Wang, X. | Philip, V. | Carter, G. | Abuzenadah, A. A. | Gari, M. | Turki, R. | Dallol, A. | Uyar, A. | Kaygun, A. | Zaman, S. | Marquez, E. | George, J. | Ucar, D. | Hendrickson, C. L. | Emerman, A. | Kraushaar, D. | Bowman, S. | Henig, N. | Davis, T. | Russello, S. | Patel, K. | Starr, D. B. | Baird, M. | Kirkpatrick, B. | Sheets, K. | Nitsche, R. | Prieto-Lafuente, L. | Landrum, M. | Lee, J. | Rubinstein, W. | Maglott, D. | Thavanati, P. K. R. | de Dios, A. Escoto | Hernandez, R. E. Navarro | Aldrate, M. E. Aguilar | Mejia, M. R. Ruiz | Kanala, K. R. R. | Abduljaleel, Z. | Khan, W. | Al-Allaf, F. A. | Athar, M. | Taher, M. M. | Shahzad, N. | Bouazzaoui, A. | Huber, E. | Dan, A. | Al-Allaf, F. A. | Herr, W. | Sprotte, G. | Köstler, J. | Hiergeist, A. | Gessner, A. | Andreesen, R. | Holler, E. | Al-Allaf, F. | Alashwal, A. | Abduljaleel, Z. | Taher, M. | Bouazzaoui, A. | Abalkhail, H. | Al-Allaf, A. | Bamardadh, R. | Athar, M. | Filiptsova, O. | Kobets, M. | Kobets, Y. | Burlaka, I. | Timoshyna, I. | Filiptsova, O. | Kobets, M. N. | Kobets, Y. | Burlaka, I. | Timoshyna, I. | Filiptsova, O. | Kobets, M. N. | Kobets, Y. | Burlaka, I. | Timoshyna, I. | Al-allaf, F. A. | Mohiuddin, M. T. | Zainularifeen, A. | Mohammed, A. | Abalkhail, H. | Owaidah, T. | Bouazzaoui, A.
Human Genomics  2016;10(Suppl 1):12.
Table of contents
O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder
A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson
O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents
R. Polimanti, J. Gelernter
O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort
X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group
O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus
A. Kapoor, D. Lee, A. Chakravarti
O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells
C. Maercker, F. Graf, M. Boutros
O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies
G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis
O7 Role of microRNA in LCL to IPSC reprogramming
S. Kumar, J. Curran, J. Blangero
O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease
S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti
O9 Metabolomic profiling for the diagnosis of neurometabolic disorders
T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea
O10 A novel causal methylation network approach to Alzheimer’s disease
Z. Hu, P. Wang, Y. Zhu, J. Zhao, M. Xiong, David A Bennett
O11 A microRNA signature identifies subtypes of triple-negative breast cancer and reveals MIR-342-3P as regulator of a lactate metabolic pathway
A. Hidalgo-Miranda, S. Romero-Cordoba, S. Rodriguez-Cuevas, R. Rebollar-Vega, E. Tagliabue, M. Iorio, E. D’Ippolito, S. Baroni
O12 Transcriptome analysis identifies genes, enhancer RNAs and repetitive elements that are recurrently deregulated across multiple cancer types
B. Kaczkowski, Y. Tanaka, H. Kawaji, A. Sandelin, R. Andersson, M. Itoh, T. Lassmann, the FANTOM5 consortium, Y. Hayashizaki, P. Carninci, A. R. R. Forrest
O13 Elevated mutation and widespread loss of constraint at regulatory and architectural binding sites across 11 tumour types
C. A. Semple
O14 Exome sequencing provides evidence of pathogenicity for genes implicated in colorectal cancer
E. A. Rosenthal, B. Shirts, L. Amendola, C. Gallego, M. Horike-Pyne, A. Burt, P. Robertson, P. Beyers, C. Nefcy, D. Veenstra, F. Hisama, R. Bennett, M. Dorschner, D. Nickerson, J. Smith, K. Patterson, D. Crosslin, R. Nassir, N. Zubair, T. Harrison, U. Peters, G. Jarvik, NHLBI GO Exome Sequencing Project
O15 The tandem duplicator phenotype as a distinct genomic configuration in cancer
F. Menghi, K. Inaki, X. Woo, P. Kumar, K. Grzeda, A. Malhotra, H. Kim, D. Ucar, P. Shreckengast, K. Karuturi, J. Keck, J. Chuang, E. T. Liu
O16 Modeling genetic interactions associated with molecular subtypes of breast cancer
B. Ji, A. Tyler, G. Ananda, G. Carter
O17 Recurrent somatic mutation in the MYC associated factor X in brain tumors
H. Nikbakht, M. Montagne, M. Zeinieh, A. Harutyunyan, M. Mcconechy, N. Jabado, P. Lavigne, J. Majewski
O18 Predictive biomarkers to metastatic pancreatic cancer treatment
J. B. Goldstein, M. Overman, G. Varadhachary, R. Shroff, R. Wolff, M. Javle, A. Futreal, D. Fogelman
O19 DDIT4 gene expression as a prognostic marker in several malignant tumors
L. Bravo, W. Fajardo, H. Gomez, C. Castaneda, C. Rolfo, J. A. Pinto
O20 Spatial organization of the genome and genomic alterations in human cancers
K. C. Akdemir, L. Chin, A. Futreal, ICGC PCAWG Structural Alterations Group
O21 Landscape of targeted therapies in solid tumors
S. Patterson, C. Statz, S. Mockus
O22 Genomic analysis reveals novel drivers and progression pathways in skin basal cell carcinoma
S. N. Nikolaev, X. I. Bonilla, L. Parmentier, B. King, F. Bezrukov, G. Kaya, V. Zoete, V. Seplyarskiy, H. Sharpe, T. McKee, A. Letourneau, P. Ribaux, K. Popadin, N. Basset-Seguin, R. Ben Chaabene, F. Santoni, M. Andrianova, M. Guipponi, M. Garieri, C. Verdan, K. Grosdemange, O. Sumara, M. Eilers, I. Aifantis, O. Michielin, F. de Sauvage, S. Antonarakis
O23 Identification of differential biomarkers of hepatocellular carcinoma and cholangiocarcinoma via transcriptome microarray meta-analysis
S. Likhitrattanapisal
O24 Clinical validity and actionability of multigene tests for hereditary cancers in a large multi-center study
S. Lincoln, A. Kurian, A. Desmond, S. Yang, Y. Kobayashi, J. Ford, L. Ellisen
O25 Correlation with tumor ploidy status is essential for correct determination of genome-wide copy number changes by SNP array
T. L. Peters, K. R. Alvarez, E. F. Hollingsworth, D. H. Lopez-Terrada
O26 Nanochannel based next-generation mapping for interrogation of clinically relevant structural variation
A. Hastie, Z. Dzakula, A. W. Pang, E. T. Lam, T. Anantharaman, M. Saghbini, H. Cao, BioNano Genomics
O27 Mutation spectrum in a pulmonary arterial hypertension (PAH) cohort and identification of associated truncating mutations in TBX4
C. Gonzaga-Jauregui, L. Ma, A. King, E. Berman Rosenzweig, U. Krishnan, J. G. Reid, J. D. Overton, F. Dewey, W. K. Chung
O28 NORTH CAROLINA macular dystrophy (MCDR1): mutations found affecting PRDM13
K. Small, A. DeLuca, F. Cremers, R. A. Lewis, V. Puech, B. Bakall, R. Silva-Garcia, K. Rohrschneider, M. Leys, F. S. Shaya, E. Stone
O29 PhenoDB and genematcher, solving unsolved whole exome sequencing data
N. L. Sobreira, F. Schiettecatte, H. Ling, E. Pugh, D. Witmer, K. Hetrick, P. Zhang, K. Doheny, D. Valle, A. Hamosh
O30 Baylor-Johns Hopkins Center for Mendelian genomics: a four year review
S. N. Jhangiani, Z. Coban Akdemir, M. N. Bainbridge, W. Charng, W. Wiszniewski, T. Gambin, E. Karaca, Y. Bayram, M. K. Eldomery, J. Posey, H. Doddapaneni, J. Hu, V. R. Sutton, D. M. Muzny, E. A. Boerwinkle, D. Valle, J. R. Lupski, R. A. Gibbs
O31 Using read overlap assembly to accurately identify structural genetic differences in an ashkenazi jewish trio
S. Shekar, W. Salerno, A. English, A. Mangubat, J. Bruestle
O32 Legal interoperability: a sine qua non for international data sharing
A. Thorogood, B. M. Knoppers, Global Alliance for Genomics and Health - Regulatory and Ethics Working Group
O33 High throughput screening platform of competent sineups: that can enhance translation activities of therapeutic target
H. Takahashi, K. R. Nitta, A. Kozhuharova, A. M. Suzuki, H. Sharma, D. Cotella, C. Santoro, S. Zucchelli, S. Gustincich, P. Carninci
O34 The undiagnosed diseases network international (UDNI): clinical and laboratory research to meet patient needs
J. J. Mulvihill, G. Baynam, W. Gahl, S. C. Groft, K. Kosaki, P. Lasko, B. Melegh, D. Taruscio
O36 Performance of computational algorithms in pathogenicity predictions for activating variants in oncogenes versus loss of function mutations in tumor suppressor genes
R. Ghosh, S. Plon
O37 Identification and electronic health record incorporation of clinically actionable pharmacogenomic variants using prospective targeted sequencing
S. Scherer, X. Qin, R. Sanghvi, K. Walker, T. Chiang, D. Muzny, L. Wang, J. Black, E. Boerwinkle, R. Weinshilboum, R. Gibbs
O38 Melanoma reprogramming state correlates with response to CTLA-4 blockade in metastatic melanoma
T. Karpinets, T. Calderone, K. Wani, X. Yu, C. Creasy, C. Haymaker, M. Forget, V. Nanda, J. Roszik, J. Wargo, L. Haydu, X. Song, A. Lazar, J. Gershenwald, M. Davies, C. Bernatchez, J. Zhang, A. Futreal, S. Woodman
O39 Data-driven refinement of complex disease classification from integration of heterogeneous functional genomics data in GeneWeaver
E. J. Chesler, T. Reynolds, J. A. Bubier, C. Phillips, M. A. Langston, E. J. Baker
O40 A general statistic framework for genome-based disease risk prediction
M. Xiong, L. Ma, N. Lin, C. Amos
O41 Integrative large-scale causal network analysis of imaging and genomic data and its application in schizophrenia studies
N. Lin, P. Wang, Y. Zhu, J. Zhao, V. Calhoun, M. Xiong
O42 Big data and NGS data analysis: the cloud to the rescue
O. Dobretsberger, M. Egger, F. Leimgruber
O43 Cpipe: a convergent clinical exome pipeline specialised for targeted sequencing
S. Sadedin, A. Oshlack, Melbourne Genomics Health Alliance
O44 A Bayesian classification of biomedical images using feature extraction from deep neural networks implemented on lung cancer data
V. A. A. Antonio, N. Ono, Clark Kendrick C. Go
O45 MAV-SEQ: an interactive platform for the Management, Analysis, and Visualization of sequence data
Z. Ahmed, M. Bolisetty, S. Zeeshan, E. Anguiano, D. Ucar
O47 Allele specific enhancer in EPAS1 intronic regions may contribute to high altitude adaptation of Tibetans
C. Zeng, J. Shao
O48 Nanochannel based next-generation mapping for structural variation detection and comparison in trios and populations
H. Cao, A. Hastie, A. W. Pang, E. T. Lam, T. Liang, K. Pham, M. Saghbini, Z. Dzakula
O49 Archaic introgression in indigenous populations of Malaysia revealed by whole genome sequencing
Y. Chee-Wei, L. Dongsheng, W. Lai-Ping, D. Lian, R. O. Twee Hee, Y. Yunus, F. Aghakhanian, S. S. Mokhtar, C. V. Lok-Yung, J. Bhak, M. Phipps, X. Shuhua, T. Yik-Ying, V. Kumar, H. Boon-Peng
O50 Breast and ovarian cancer prevention: is it time for population-based mutation screening of high risk genes?
I. Campbell, M.-A. Young, P. James, Lifepool
O53 Comprehensive coverage from low DNA input using novel NGS library preparation methods for WGS and WGBS
C. Schumacher, S. Sandhu, T. Harkins, V. Makarov
O54 Methods for large scale construction of robust PCR-free libraries for sequencing on Illumina HiSeqX platform
H. DoddapaneniR. Glenn, Z. Momin, B. Dilrukshi, H. Chao, Q. Meng, B. Gudenkauf, R. Kshitij, J. Jayaseelan, C. Nessner, S. Lee, K. Blankenberg, L. Lewis, J. Hu, Y. Han, H. Dinh, S. Jireh, K. Walker, E. Boerwinkle, D. Muzny, R. Gibbs
O55 Rapid capture methods for clinical sequencing
J. Hu, K. Walker, C. Buhay, X. Liu, Q. Wang, R. Sanghvi, H. Doddapaneni, Y. Ding, N. Veeraraghavan, Y. Yang, E. Boerwinkle, A. L. Beaudet, C. M. Eng, D. M. Muzny, R. A. Gibbs
O56 A diploid personal human genome model for better genomes from diverse sequence data
K. C. C. Worley, Y. Liu, D. S. T. Hughes, S. C. Murali, R. A. Harris, A. C. English, X. Qin, O. A. Hampton, P. Larsen, C. Beck, Y. Han, M. Wang, H. Doddapaneni, C. L. Kovar, W. J. Salerno, A. Yoder, S. Richards, J. Rogers, J. R. Lupski, D. M. Muzny, R. A. Gibbs
O57 Development of PacBio long range capture for detection of pathogenic structural variants
Q. Meng, M. Bainbridge, M. Wang, H. Doddapaneni, Y. Han, D. Muzny, R. Gibbs
O58 Rhesus macaques exhibit more non-synonymous variation but greater impact of purifying selection than humans
R. A. Harris, M. Raveenedran, C. Xue, M. Dahdouli, L. Cox, G. Fan, B. Ferguson, J. Hovarth, Z. Johnson, S. Kanthaswamy, M. Kubisch, M. Platt, D. Smith, E. Vallender, R. Wiseman, X. Liu, J. Below, D. Muzny, R. Gibbs, F. Yu, J. Rogers
O59 Assessing RNA structure disruption induced by single-nucleotide variation
J. Lin, Y. Zhang, Z. Ouyang
P1 A meta-analysis of genome-wide association studies of mitochondrial dna copy number
A. Moore, Z. Wang, J. Hofmann, M. Purdue, R. Stolzenberg-Solomon, S. Weinstein, D. Albanes, C.-S. Liu, W.-L. Cheng, T.-T. Lin, Q. Lan, N. Rothman, S. Berndt
P2 Missense polymorphic genetic combinations underlying down syndrome susceptibility
E. S. Chen
P4 The evaluation of alteration of ELAM-1 expression in the endometriosis patients
H. Bahrami, A. Khoshzaban, S. Heidari Keshal
P5 Obesity and the incidence of apolipoprotein E polymorphisms in an assorted population from Saudi Arabia population
K. K. R. Alharbi
P6 Genome-associated personalized antithrombotical therapy for patients with high risk of thrombosis and bleeding
M. Zhalbinova, A. Akilzhanova, S. Rakhimova, M. Bekbosynova, S. Myrzakhmetova
P7 Frequency of Xmn1 polymorphism among sickle cell carrier cases in UAE population
M. Matar
P8 Differentiating inflammatory bowel diseases by using genomic data: dimension of the problem and network organization
N. Mili, R. Molinari, Y. Ma, S. Guerrier
P9 Vulnerability of genetic variants to the risk of autism among Saudi children
N. Elhawary, M. Tayeb, N. Bogari, N. Qotb
P10 Chromatin profiles from ex vivo purified dopaminergic neurons establish a promising model to support studies of neurological function and dysfunction
S. A. McClymont, P. W. Hook, L. A. Goff, A. McCallion
P11 Utilization of a sensitized chemical mutagenesis screen to identify genetic modifiers of retinal dysplasia in homozygous Nr2e3rd7 mice
Y. Kong, J. R. Charette, W. L. Hicks, J. K. Naggert, L. Zhao, P. M. Nishina
P12 Ion torrent next generation sequencing of recessive polycystic kidney disease in Saudi patients
B. M. Edrees, M. Athar, F. A. Al-Allaf, M. M. Taher, W. Khan, A. Bouazzaoui, N. A. Harbi, R. Safar, H. Al-Edressi, A. Anazi, N. Altayeb, M. A. Ahmed, K. Alansary, Z. Abduljaleel
P13 Digital expression profiling of Purkinje neurons and dendrites in different subcellular compartments
A. Kratz, P. Beguin, S. Poulain, M. Kaneko, C. Takahiko, A. Matsunaga, S. Kato, A. M. Suzuki, N. Bertin, T. Lassmann, R. Vigot, P. Carninci, C. Plessy, T. Launey
P14 The evolution of imperfection and imperfection of evolution: the functional and functionless fractions of the human genome
D. Graur
P16 Species-independent identification of known and novel recurrent genomic entities in multiple cancer patients
J. Friis-Nielsen, J. M. Izarzugaza, S. Brunak
P18 Discovery of active gene modules which are densely conserved across multiple cancer types reveal their prognostic power and mutually exclusive mutation patterns
B. S. Soibam
P19 Whole exome sequencing of dysplastic leukoplakia tissue indicates sequential accumulation of somatic mutations from oral precancer to cancer
D. Das, N. Biswas, S. Das, S. Sarkar, A. Maitra, C. Panda, P. Majumder
P21 Epigenetic mechanisms of carcinogensis by hereditary breast cancer genes
J. J. Gruber, N. Jaeger, M. Snyder
P22 RNA direct: a novel RNA enrichment strategy applied to transcripts associated with solid tumors
K. Patel, S. Bowman, T. Davis, D. Kraushaar, A. Emerman, S. Russello, N. Henig, C. Hendrickson
P23 RNA sequencing identifies gene mutations for neuroblastoma
K. Zhang
P24 Participation of SFRP1 in the modulation of TMPRSS2-ERG fusion gene in prostate cancer cell lines
M. Rodriguez-Dorantes, C. D. Cruz-Hernandez, C. D. P. Garcia-Tobilla, S. Solorzano-Rosales
P25 Targeted Methylation Sequencing of Prostate Cancer
N. Jäger, J. Chen, R. Haile, M. Hitchins, J. D. Brooks, M. Snyder
P26 Mutant TPMT alleles in children with acute lymphoblastic leukemia from México City and Yucatán, Mexico
S. Jiménez-Morales, M. Ramírez, J. Nuñez, V. Bekker, Y. Leal, E. Jiménez, A. Medina, A. Hidalgo, J. Mejía
P28 Genetic modifiers of Alström syndrome
J. Naggert, G. B. Collin, K. DeMauro, R. Hanusek, P. M. Nishina
P31 Association of genomic variants with the occurrence of angiotensin-converting-enzyme inhibitor (ACEI)-induced coughing among Filipinos
E. M. Cutiongco De La Paz, R. Sy, J. Nevado, P. Reganit, L. Santos, J. D. Magno, F. E. Punzalan , D. Ona , E. Llanes, R. L. Santos-Cortes , R. Tiongco, J. Aherrera, L. Abrahan, P. Pagauitan-Alan; Philippine Cardiogenomics Study Group
P32 The use of “humanized” mouse models to validate disease association of a de novo GARS variant and to test a novel gene therapy strategy for Charcot-Marie-Tooth disease type 2D
K. H. Morelli, J. S. Domire, N. Pyne, S. Harper, R. Burgess
P34 Molecular regulation of chondrogenic human induced pluripotent stem cells
M. A. Gari, A. Dallol, H. Alsehli, A. Gari, M. Gari, A. Abuzenadah
P35 Molecular profiling of hematologic malignancies: implementation of a variant assessment algorithm for next generation sequencing data analysis and clinical reporting
M. Thomas, M. Sukhai, S. Garg, M. Misyura, T. Zhang, A. Schuh, T. Stockley, S. Kamel-Reid
P36 Accessing genomic evidence for clinical variants at NCBI
S. Sherry, C. Xiao, D. Slotta, K. Rodarmer, M. Feolo, M. Kimelman, G. Godynskiy, C. O’Sullivan, E. Yaschenko
P37 NGS-SWIFT: a cloud-based variant analysis framework using control-accessed sequencing data from DBGAP/SRA
C. Xiao, E. Yaschenko, S. Sherry
P38 Computational assessment of drug induced hepatotoxicity through gene expression profiling
C. Rangel-Escareño, H. Rueda-Zarate
P40 Flowr: robust and efficient pipelines using a simple language-agnostic approach;ultraseq; fast modular pipeline for somatic variation calling using flowr
S. Seth, S. Amin, X. Song, X. Mao, H. Sun, R. G. Verhaak, A. Futreal, J. Zhang
P41 Applying “Big data” technologies to the rapid analysis of heterogenous large cohort data
S. J. Whiite, T. Chiang, A. English, J. Farek, Z. Kahn, W. Salerno, N. Veeraraghavan, E. Boerwinkle, R. Gibbs
P42 FANTOM5 web resource for the large-scale genome-wide transcription start site activity profiles of wide-range of mammalian cells
T. Kasukawa, M. Lizio, J. Harshbarger, S. Hisashi, J. Severin, A. Imad, S. Sahin, T. C. Freeman, K. Baillie, A. Sandelin, P. Carninci, A. R. R. Forrest, H. Kawaji, The FANTOM Consortium
P43 Rapid and scalable typing of structural variants for disease cohorts
W. Salerno, A. English, S. N. Shekar, A. Mangubat, J. Bruestle, E. Boerwinkle, R. A. Gibbs
P44 Polymorphism of glutathione S-transferases and sulphotransferases genes in an Arab population
A. H. Salem, M. Ali, A. Ibrahim, M. Ibrahim
P46 Genetic divergence of CYP3A5*3 pharmacogenomic marker for native and admixed Mexican populations
J. C. Fernandez-Lopez, V. Bonifaz-Peña, C. Rangel-Escareño, A. Hidalgo-Miranda, A. V. Contreras
P47 Whole exome sequence meta-analysis of 13 white blood cell, red blood cell, and platelet traits
L. Polfus, CHARGE and NHLBI Exome Sequence Project Working Groups
P48 Association of adipoq gene with type 2 diabetes and related phenotypes in african american men and women: The jackson heart study
S. Davis, R. Xu, S. Gebeab, P Riestra, A Gaye, R. Khan, J. Wilson, A. Bidulescu
P49 Common variants in casr gene are associated with serum calcium levels in koreans
S. H. Jung, N. Vinayagamoorthy, S. H. Yim, Y. J. Chung
P50 Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with multiple exponential functions
Y. Zhou, S. Xu
P51 A Bayesian framework for generalized linear mixed models in genome-wide association studies
X. Wang, V. Philip, G. Carter
P52 Targeted sequencing approach for the identification of the genetic causes of hereditary hearing impairment
A. A. Abuzenadah, M. Gari, R. Turki, A. Dallol
P53 Identification of enhancer sequences by ATAC-seq open chromatin profiling
A. Uyar, A. Kaygun, S. Zaman, E. Marquez, J. George, D. Ucar
P54 Direct enrichment for the rapid preparation of targeted NGS libraries
C. L. Hendrickson, A. Emerman, D. Kraushaar, S. Bowman, N. Henig, T. Davis, S. Russello, K. Patel
P56 Performance of the Agilent D5000 and High Sensitivity D5000 ScreenTape assays for the Agilent 4200 Tapestation System
R. Nitsche, L. Prieto-Lafuente
P57 ClinVar: a multi-source archive for variant interpretation
M. Landrum, J. Lee, W. Rubinstein, D. Maglott
P59 Association of functional variants and protein physical interactions of human MUTY homolog linked with familial adenomatous polyposis and colorectal cancer syndrome
Z. Abduljaleel, W. Khan, F. A. Al-Allaf, M. Athar , M. M. Taher, N. Shahzad
P60 Modification of the microbiom constitution in the gut using chicken IgY antibodies resulted in a reduction of acute graft-versus-host disease after experimental bone marrow transplantation
A. Bouazzaoui, E. Huber, A. Dan, F. A. Al-Allaf, W. Herr, G. Sprotte, J. Köstler, A. Hiergeist, A. Gessner, R. Andreesen, E. Holler
P61 Compound heterozygous mutation in the LDLR gene in Saudi patients suffering severe hypercholesterolemia
F. Al-Allaf, A. Alashwal, Z. Abduljaleel, M. Taher, A. Bouazzaoui, H. Abalkhail, A. Al-Allaf, R. Bamardadh, M. Athar
PMCID: PMC4896275  PMID: 27294413
18.  The RICORDO approach to semantic interoperability for biomedical data and models: strategy, standards and solutions 
BMC Research Notes  2011;4:313.
The practice and research of medicine generates considerable quantities of data and model resources (DMRs). Although in principle biomedical resources are re-usable, in practice few can currently be shared. In particular, the clinical communities in physiology and pharmacology research, as well as medical education, (i.e. PPME communities) are facing considerable operational and technical obstacles in sharing data and models.
We outline the efforts of the PPME communities to achieve automated semantic interoperability for clinical resource documentation in collaboration with the RICORDO project. Current community practices in resource documentation and knowledge management are overviewed. Furthermore, requirements and improvements sought by the PPME communities to current documentation practices are discussed. The RICORDO plan and effort in creating a representational framework and associated open software toolkit for the automated management of PPME metadata resources is also described.
RICORDO is providing the PPME community with tools to effect, share and reason over clinical resource annotations. This work is contributing to the semantic interoperability of DMRs through ontology-based annotation by (i) supporting more effective navigation and re-use of clinical DMRs, as well as (ii) sustaining interoperability operations based on the criterion of biological similarity. Operations facilitated by RICORDO will range from automated dataset matching to model merging and managing complex simulation workflows. In effect, RICORDO is contributing to community standards for resource sharing and interoperability.
PMCID: PMC3192696  PMID: 21878109
19.  Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: The SHARPn project 
Journal of biomedical informatics  2012;45(4):763-771.
The Strategic Health IT Advanced Research Projects (SHARP) Program, established by the Office of the National Coordinator for Health Information Technology in 2010 supports research findings that remove barriers for increased adoption of health IT. The improvements envisioned by the SHARP Area 4 Consortium (SHARPn) will enable the use of the electronic health record (EHR) for secondary purposes, such as care process and outcomes improvement, biomedical research and epidemiologic monitoring of the nation’s health. One of the primary informatics problem areas in this endeavor is the standardization of disparate health data from the nation’s many health care organizations and providers. The SHARPn team is developing open source services and components to support the ubiquitous exchange, sharing and reuse or ‘liquidity’ of operational clinical data stored in electronic health records. One year into the design and development of the SHARPn framework, we demonstrated end to end data flow and a prototype SHARPn platform, using thousands of patient electronic records sourced from two large healthcare organizations: Mayo Clinic and Intermountain Healthcare. The platform was deployed to (1) receive source EHR data in several formats, (2) generate structured data from EHR narrative text, and (3) normalize the EHR data using common detailed clinical models and Consolidated Health Informatics standard terminologies, which were (4) accessed by a phenotyping service using normalized data specifications. The architecture of this prototype SHARPn platform is presented. The EHR data throughput demonstration showed success in normalizing native EHR data, both structured and narrative, from two independent organizations and EHR systems. Based on the demonstration, observed challenges for standardization of EHR data for interoperable secondary use are discussed.
PMCID: PMC4905766  PMID: 22326800
Electronic health records; Meaningful use; Health information exchange; High-throughput phenotyping; Natural language processing
20.  ARGOS Policy Brief on Semantic Interoperability 
Semantic interoperability is one of the priority themes of the ARGOS Trans-Atlantic Observatory. This topic represents a globally recognised challenge that must be addressed if electronic health records are to be shared among heterogeneous systems, and the information in them exploited to the maximum benefit of patients, professionals, health services, research, and industry. Progress in this multi-faceted challenge has been piecemeal, and valuable lessons have been learned, and approaches discovered, in Europe and in the US that can be shared and combined.
Experts from both continents have met at three ARGOS workshops during 2010 and 2011 to share understanding of these issues and how they might be tackled collectively from both sides of the Atlantic. This policy brief summarises the problems and the reasons why they are important to tackle, and also why they are so difficult. It outlines the major areas of semantic innovation that exist and that are available to help address this challenge. It proposes a series of next steps that need to be championed on both sides of the Atlantic if further progress is to be made in sharing and analysing electronic health records meaningfully.
Semantic interoperability requires the use of standards, not only for EHR data to be transferred and structurally mapped into a receiving repository, but also for the clinical content of the EHR to be interpreted in conformity with the original meanings intended by its authors. Wide-scale engagement with professional bodies, globally, is needed to develop these clinical information standards. Accurate and complete clinical documentation, faithful to the patient’s situation, and interoperability between systems, require widespread and dependable access to published and maintained collections of coherent and quality-assured semantic resources, including models such as archetypes and templates that would (1) provide clinical context, (2) be mapped to interoperability standards for EHR data, (3) be linked to well specified multi-lingual terminology value sets, and (4) be derived from high quality ontologies.
There is need to gain greater experience in how semantic resources should be defined, validated, and disseminated, how users (who increasingly will include patients) should be educated to improve the quality and consistency of EHR documentation and to make full use of it. There are urgent needs to scale up the authorship, acceptance, and adoption of clinical information standards, to leverage and harmonise the islands of standardisation optimally, to assure the quality of the artefacts produced, and to organise end-to-end governance of the development and adoption of solutions.
PMCID: PMC4896070  PMID: 21893897
Electronic health records; interoperability; knowledge representation
21.  Interoperability of Information Systems Managed and Used by the Local Health Departments 
This research study describes the level of interoperability of local health department information systems and identifies factors associated with lack of interoperability.
In the post-Affordable Care Act era marked by interorganizational collaborations and availability of large amounts of electronic data from other community partners, it is imperative to assess the interoperability of information systems used by the local health departments (LHDs).
To describe the level of interoperability of LHD information systems and identify factors associated with lack of interoperability.
Data and Methods:
This mixed-methods research uses data from the 2015 Informatics Capacity and Needs Assessment Survey, with a target population of all LHDs in the United States. A representative sample of 650 LHDs was drawn using a stratified random sampling design. A total of 324 completed responses were received (50% response rate). Qualitative data were used from a key informant interview study of LHD informatics staff from across the United States. Qualitative data were independently coded by 2 researchers and analyzed thematically. Survey data were cleaned, bivariate comparisons were conducted, and a multivariable logistic regression was run to characterize factors associated with interoperability.
For 30% of LHDs, no systems were interoperable, and 38% of LHD respondents indicated some of the systems were interoperable. Significant determinants of interoperability included LHDs having leadership support (adjusted odds ratio [AOR] = 3.54), control of information technology budget allocation (AOR = 2.48), control of data systems (AOR = 2.31), having a strategic plan for information systems (AOR = 1.92), and existence of business process analysis and redesign (AOR = 1.49).
Interoperability of all systems may be an informatics goal, but only a small proportion of LHDs reported having interoperable systems, pointing to a substantial need among LHDs nationwide.
PMCID: PMC5049946  PMID: 27684616
business process analysis; informatics; information systems; information technology; interoperability; IT infrastructure; local health departments; local public health agencies
22.  Boundaries and e-health implementation in health and social care 
The major problem facing health and social care systems globally today is the growing challenge of an elderly population with complex health and social care needs. A longstanding challenge to the provision of high quality, effectively coordinated care for those with complex needs has been the historical separation of health and social care. Access to timely and accurate data about patients and their treatments has the potential to deliver better care at less cost.
To explore the way in which structural, professional and geographical boundaries have affected e-health implementation in health and social care, through an empirical study of the implementation of an electronic version of Single Shared Assessment (SSA) in Scotland, using three retrospective, qualitative case studies in three different health board locations.
Progress in effectively sharing electronic data had been slow and uneven. One cause was the presence of established structural boundaries, which lead to competing priorities, incompatible IT systems and infrastructure, and poor cooperation. A second cause was the presence of established professional boundaries, which affect staffs’ understanding and acceptance of data sharing and their information requirements. Geographical boundaries featured but less prominently and contrasting perspectives were found with regard to issues such as co-location of health and social care professionals.
To provide holistic care to those with complex health and social care needs, it is essential that we develop integrated approaches to care delivery. Successful integration needs practices such as good project management and governance, ensuring system interoperability, leadership, good training and support, together with clear efforts to improve working relations across professional boundaries and communication of a clear project vision. This study shows that while technological developments make integration possible, long-standing boundaries constitute substantial risks to IT implementations across the health and social care interface which those initiating major changes would do well to consider before committing to the investment.
PMCID: PMC3465217  PMID: 22958223
23.  An Information System for European culture collections: the way forward 
SpringerPlus  2016;5(1):772.
Culture collections contain indispensable information about the microorganisms preserved in their repositories, such as taxonomical descriptions, origins, physiological and biochemical characteristics, bibliographic references, etc. However, information currently accessible in databases rarely adheres to common standard protocols. The resultant heterogeneity between culture collections, in terms of both content and format, notably hampers microorganism-based research and development (R&D). The optimized exploitation of these resources thus requires standardized, and simplified, access to the associated information. To this end, and in the interest of supporting R&D in the fields of agriculture, health and biotechnology, a pan-European distributed research infrastructure, MIRRI, including over 40 public culture collections and research institutes from 19 European countries, was established. A prime objective of MIRRI is to unite and provide universal access to the fragmented, and untapped, resources, information and expertise available in European public collections of microorganisms; a key component of which is to develop a dynamic Information System. For the first time, both culture collection curators as well as their users have been consulted and their feedback, concerning the needs and requirements for collection databases and data accessibility, utilised. Users primarily noted that databases were not interoperable, thus rendering a global search of multiple databases impossible. Unreliable or out-of-date and, in particular, non-homogenous, taxonomic information was also considered to be a major obstacle to searching microbial data efficiently. Moreover, complex searches are rarely possible in online databases thus limiting the extent of search queries. Curators also consider that overall harmonization—including Standard Operating Procedures, data structure, and software tools—is necessary to facilitate their work and to make high-quality data easily accessible to their users. Clearly, the needs of culture collection curators coincide with those of users on the crucial point of database interoperability. In this regard, and in order to design an appropriate Information System, important aspects on which the culture collection community should focus include: the interoperability of data sets with the ontologies to be used; setting best practice in data management, and the definition of an appropriate data standard.
PMCID: PMC4912550  PMID: 27386258
Culture collection; Microorganism; Information Technology; Microbial domain Biological Resource Centre
24.  Argo: an integrative, interactive, text mining-based workbench supporting curation 
Curation of biomedical literature is often supported by the automatic analysis of textual content that generally involves a sequence of individual processing components. Text mining (TM) has been used to enhance the process of manual biocuration, but has been focused on specific databases and tasks rather than an environment integrating TM tools into the curation pipeline, catering for a variety of tasks, types of information and applications. Processing components usually come from different sources and often lack interoperability. The well established Unstructured Information Management Architecture is a framework that addresses interoperability by defining common data structures and interfaces. However, most of the efforts are targeted towards software developers and are not suitable for curators, or are otherwise inconvenient to use on a higher level of abstraction. To overcome these issues we introduce Argo, an interoperable, integrative, interactive and collaborative system for text analysis with a convenient graphic user interface to ease the development of processing workflows and boost productivity in labour-intensive manual curation. Robust, scalable text analytics follow a modular approach, adopting component modules for distinct levels of text analysis. The user interface is available entirely through a web browser that saves the user from going through often complicated and platform-dependent installation procedures. Argo comes with a predefined set of processing components commonly used in text analysis, while giving the users the ability to deposit their own components. The system accommodates various areas and levels of user expertise, from TM and computational linguistics to ontology-based curation. One of the key functionalities of Argo is its ability to seamlessly incorporate user-interactive components, such as manual annotation editors, into otherwise completely automatic pipelines. As a use case, we demonstrate the functionality of an in-built manual annotation editor that is well suited for in-text corpus annotation tasks.
Database URL:
PMCID: PMC3308166  PMID: 22434844
25.  Programming biological models in Python using PySB 
PySB is a framework for creating biological models as Python programs using a high-level, action-oriented vocabulary that promotes transparency, extensibility and reusability. PySB interoperates with many existing modeling tools and supports distributed model development.
PySB models are programs and leverage existing programming tools for documentation, testing, and collaborative development.Reusable functions can encode common low-level biochemical processes as well as high-level modules, making models transparent and concise.Modeling workflow is accelerated through close integration with Python numerical tools and interoperability with existing modeling software.We demonstrate the use of PySB to encode 15 alternative hypotheses for the mitochondrial regulation of apoptosis, including a new ‘Embedded Together' model based on recent biochemical findings.
Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis.
PMCID: PMC3588907  PMID: 23423320
apoptosis; modeling; rule-based; software engineering

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