Motivation: Finding protein-protein interaction (PPI) information from literature is challenging but an important issue. However, keyword search in PubMed® is often time consuming because it requires a series of actions that refine keywords and browse search results until it reaches a goal. Due to the rapid growth of biomedical literature, it has become more difficult for biologists and curators to locate PPI information quickly. Therefore, a tool for prioritizing PPI informative articles can be a useful assistant for finding this PPI-relevant information.
Results: PIE (Protein Interaction information Extraction) the search is a web service implementing a competition-winning approach utilizing word and syntactic analyses by machine learning techniques. For easy user access, PIE the search provides a PubMed-like search environment, but the output is the list of articles prioritized by PPI confidence scores. By obtaining PPI-related articles at high rank, researchers can more easily find the up-to-date PPI information, which cannot be found in manually curated PPI databases.
Supplementary data are available at Bioinformatics online.
This article reports on a detailed investigation of PubMed users’ needs and behavior as a step toward improving biomedical information retrieval. PubMed is providing free service to researchers with access to more than 19 million citations for biomedical articles from MEDLINE and life science journals. It is accessed by millions of users each day. Efficient search tools are crucial for biomedical researchers to keep abreast of the biomedical literature relating to their own research. This study provides insight into PubMed users’ needs and their behavior. This investigation was conducted through the analysis of one month of log data, consisting of more than 23 million user sessions and more than 58 million user queries. Multiple aspects of users’ interactions with PubMed are characterized in detail with evidence from these logs. Despite having many features in common with general Web searches, biomedical information searches have unique characteristics that are made evident in this study. PubMed users are more persistent in seeking information and they reformulate queries often. The three most frequent types of search are search by author name, search by gene/protein, and search by disease. Use of abbreviation in queries is very frequent. Factors such as result set size influence users’ decisions. Analysis of characteristics such as these plays a critical role in identifying users’ information needs and their search habits. In turn, such an analysis also provides useful insight for improving biomedical information retrieval.
Database URL: http://www.ncbi.nlm.nih.gov/PubMed
The use of at least two complementary online biomedical databases is generally considered critical for biomedical scientists seeking to keep fully abreast of recent research developments as well as to retrieve the highest number of relevant citations possible. Although the National Library of Medicine's MEDLINE is usually the database of choice, this paper illustrates the benefits of using another database, the Institute for Scientific Information's SciSearch, when conducting a biomedical information search. When a simple query about red wine consumption and coronary artery disease was posed simultaneously in both MEDLINE and SciSearch, a greater number of relevant citations were retrieved through SciSearch. This paper also provides suggestions for carrying out a comprehensive biomedical literature search in a rapid and efficient manner by using SciSearch in conjunction with MEDLINE.
Biomedical event extraction has attracted substantial attention as it can assist researchers in understanding the plethora of interactions among genes that are described in publications in molecular biology. While most recent work has focused on abstracts, the BioNLP 2011 shared task evaluated the submitted systems on both abstracts and full papers. In this article, we describe our submission to the shared task which decomposes event extraction into a set of classification tasks that can be learned either independently or jointly using the search-based structured prediction framework. Our intention is to explore how these two learning paradigms compare in the context of the shared task.
We report that models learned using search-based structured prediction exceed the accuracy of independently learned classifiers by 8.3 points in F-score, with the gains being more pronounced on the more complex Regulation events (13.23 points). Furthermore, we show how the trade-off between recall and precision can be adjusted in both learning paradigms and that search-based structured prediction achieves better recall at all precision points. Finally, we report on experiments with a simple domain-adaptation method, resulting in the second-best performance achieved by a single system.
We demonstrate that joint inference using the search-based structured prediction framework can achieve better performance than independently learned classifiers, thus demonstrating the potential of this learning paradigm for event extraction and other similarly complex information-extraction tasks.
Protein–protein interaction (PPI) extraction has been an important research topic in bio-text mining area, since the PPI information is critical for understanding biological processes. However, there are very few open systems available on the Web and most of the systems focus on keyword searching based on predefined PPIs. PIE (Protein Interaction information Extraction system) is a configurable Web service to extract PPIs from literature, including user-provided papers as well as PubMed articles. After providing abstracts or papers, the prediction results are displayed in an easily readable form with essential, yet compact features. The PIE interface supports more features such as PDF file extraction, PubMed search tool and network communication, which are useful for biologists and bio-system developers. The PIE system utilizes natural language processing techniques and machine learning methodologies to predict PPI sentences, which results in high precision performance for Web users. PIE is freely available at http://bi.snu.ac.kr/pie/.
Negation occurs frequently in scientific literature, especially in biomedical literature. It has previously been reported that around 13% of sentences found in biomedical research articles contain negation. Historically, the main motivation for identifying negated events has been to ensure their exclusion from lists of extracted interactions. However, recently, there has been a growing interest in negative results, which has resulted in negation detection being identified as a key challenge in biomedical relation extraction. In this article, we focus on the problem of identifying negated bio-events, given gold standard event annotations.
We have conducted a detailed analysis of three open access bio-event corpora containing negation information (i.e., GENIA Event, BioInfer and BioNLP’09 ST), and have identified the main types of negated bio-events. We have analysed the key aspects of a machine learning solution to the problem of detecting negated events, including selection of negation cues, feature engineering and the choice of learning algorithm. Combining the best solutions for each aspect of the problem, we propose a novel framework for the identification of negated bio-events. We have evaluated our system on each of the three open access corpora mentioned above. The performance of the system significantly surpasses the best results previously reported on the BioNLP’09 ST corpus, and achieves even better results on the GENIA Event and BioInfer corpora, both of which contain more varied and complex events.
Recently, in the field of biomedical text mining, the development and enhancement of event-based systems has received significant interest. The ability to identify negated events is a key performance element for these systems. We have conducted the first detailed study on the analysis and identification of negated bio-events. Our proposed framework can be integrated with state-of-the-art event extraction systems. The resulting systems will be able to extract bio-events with attached polarities from textual documents, which can serve as the foundation for more elaborate systems that are able to detect mutually contradicting bio-events.
The goal of text mining is to make the information conveyed in scientific publications accessible to structured search and automatic analysis. Two important subtasks of text mining are entity mention normalization - to identify biomedical objects in text - and extraction of qualified relationships between those objects. We describe a method for identifying genes and relationships between proteins.
We present solutions to gene mention normalization and extraction of protein-protein interactions. For the first task, we identify genes by using background knowledge on each gene, namely annotations related to function, location, disease, and so on. Our approach currently achieves an f-measure of 86.4% on the BioCreative II gene normalization data. For the extraction of protein-protein interactions, we pursue an approach that builds on classical sequence analysis: motifs derived from multiple sequence alignments. The method achieves an f-measure of 24.4% (micro-average) in the BioCreative II interaction pair subtask.
For gene mention normalization, our approach outperforms strategies that utilize only the matching of genes names against dictionaries, without invoking further knowledge on each gene. Motifs derived from alignments of sentences are successful at identifying protein interactions in text; the approach we present in this report is fully automated and performs similarly to systems that require human intervention at one or more stages.
Our methods for gene, protein, and species identification, and extraction of protein-protein are available as part of the BioCreative Meta Services (BCMS), see .
BioSYNTHESIS is a prototype intelligent retrieval system under development as part of the IAIMS project at Georgetown University. The aim is to create an integrated system that can retrieve information located on disparate computer systems. The project work has been divided in two phases: BioSYNTHESIS I, development of a single menu to access various databases which reside on different computers; and BioSYNTHESIS II, development of a search component that facilitates complex searching for the user. BioSYNTHESIS II will accept a user's query and conduct a search for appropriate information in the IAIMS databases at Georgetown. For information not available at Georgetown, such as full text, it will access selected remote systems and translate the search query as appropriate for the target system. The search through various computer systems and different databases with unique storage and retrieval structures will be transparent to the user. BioSYNTHESIS I is complete and available to users. The design work for BioSYNTHESIS II is under development and will continue as a multiyear technical research effort of the proposed Georgetown IAIMS implementation project.
The increasing number of scientific literature on the Web and the absence of efficient tools used for classifying and searching the documents are the two most important factors that influence the speed of the search and the quality of the results. Previous studies have shown that the usage of ontologies makes it possible to process document and query information at the semantic level, which greatly improves the search for the relevant information and makes one step further towards the Semantic Web. A fundamental step in these approaches is the annotation of documents with ontology concepts, which can also be seen as a classification task. In this paper we address this issue for the biomedical domain and present a new automated and robust method, based on a Maximum Entropy approach, for annotating biomedical literature documents with terms from the Medical Subject Headings (MeSH).
The experimental evaluation shows that the suggested Maximum Entropy approach for annotating biomedical documents with MeSH terms is highly accurate, robust to the ambiguity of terms, and can provide very good performance even when a very small number of training documents is used. More precisely, we show that the proposed algorithm obtained an average F-measure of 92.4% (precision 99.41%, recall 86.77%) for the full range of the explored terms (4,078 MeSH terms), and that the algorithm’s performance is resilient to terms’ ambiguity, achieving an average F-measure of 92.42% (precision 99.32%, recall 86.87%) in the explored MeSH terms which were found to be ambiguous according to the Unified Medical Language System (UMLS) thesaurus. Finally, we compared the results of the suggested methodology with a Naive Bayes and a Decision Trees classification approach, and we show that the Maximum Entropy based approach performed with higher F-Measure in both ambiguous and monosemous MeSH terms.
Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.
This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard.
The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring.
In the big data era, biomedical research continues to generate a large amount of data, and the generated information is often stored in a database and made publicly available. Although combining data from multiple databases should accelerate further studies, the current number of life sciences databases is too large to grasp features and contents of each database.
We have developed Sagace, a web-based search engine that enables users to retrieve information from a range of biological databases (such as gene expression profiles and proteomics data) and biological resource banks (such as mouse models of disease and cell lines). With Sagace, users can search more than 300 databases in Japan. Sagace offers features tailored to biomedical research, including manually tuned ranking, a faceted navigation to refine search results, and rich snippets constructed with retrieved metadata for each database entry.
Sagace will be valuable for experts who are involved in biomedical research and drug development in both academia and industry. Sagace is freely available at
Search engine; Biomedical data; Biomedical resources; Faceted search; Microdata
Over recent years, there has been a growing interest in extracting information automatically
or semi-automatically from the scientific literature. This paper describes
a novel ontology-based interactive information extraction (OBIIE) framework and a
specific OBIIE system. We describe how this system enables life scientists to make
ad hoc queries similar to using a standard search engine, but where the results are
obtained in a database format similar to a pre-programmed information extraction
engine. We present a case study in which the system was evaluated for extracting
co-factors from EMBASE and MEDLINE.
Genome-wide association studies (GWAS) have successfully identified numerous genetic loci that are associated with phenotypic traits and diseases. GWAS Integrator is a bioinformatics tool that integrates information on these associations from the National Human Genome Research institute (NHGRI) Catalog, SNAP (SNP Annotation and Proxy Search), and the Human Genome Epidemiology (HuGE) Navigator literature database. This tool includes robust search and data mining functionalities that can be used to quickly identify relevant associations from GWAS, as well as proxy single-nucleotide polymorphisms (SNPs) and potential candidate genes. Query-based University of California Santa Cruz (UCSC) Genome Browser custom tracks are generated dynamically on the basis of users' selected GWAS hits or candidate genes from HuGE Navigator literature database (http://www.hugenavigator.net/HuGENavigator/gWAHitStartPage.do). The GWAS Integrator may help enhance inference on potential genetic associations identified from GWAS studies.
genome-wide association studies; database; bioinformatics
BioMart is a freely available, open source, federated database system that provides a unified access to disparate, geographically distributed data sources. It is designed to be data agnostic and platform independent, such that existing databases can easily be incorporated into the BioMart framework. BioMart allows databases hosted on different servers to be presented seamlessly to users, facilitating collaborative projects between different research groups. BioMart contains several levels of query optimization to efficiently manage large data sets and offers a diverse selection of graphical user interfaces and application programming interfaces to ensure that queries can be performed in whatever manner is most convenient for the user. The software has now been adopted by a large number of different biological databases spanning a wide range of data types and providing a rich source of annotation available to bioinformaticians and biologists alike.
Database URL: http://www.biomart.org
The range of publicly available biomedical data is enormous and is expanding fast. This expansion means that researchers now face a hurdle to extracting the data they need from the large numbers of data that are available. Biomedical researchers have turned to ontologies and terminologies to structure and annotate their data with ontology concepts for better search and retrieval. However, this annotation process cannot be easily automated and often requires expert curators. Plus, there is a lack of easy-to-use systems that facilitate the use of ontologies for annotation. This paper presents the Open Biomedical Annotator (OBA), an ontology-based Web service that annotates public datasets with biomedical ontology concepts based on their textual metadata (www.bioontology.org). The biomedical community can use the annotator service to tag datasets automatically with ontology terms (from UMLS and NCBO BioPortal ontologies). Such annotations facilitate translational discoveries by integrating annotated data.
The tasks in BioCreative II were designed to approximate some of the laborious work involved in curating biomedical research papers. The approach to these tasks taken by the University of Edinburgh team was to adapt and extend the existing natural language processing (NLP) system that we have developed as part of a commercial curation assistant. Although this paper concentrates on using NLP to assist with curation, the system can be equally employed to extract types of information from the literature that is immediately relevant to biologists in general.
Our system was among the highest performing on the interaction subtasks, and competitive performance on the gene mention task was achieved with minimal development effort. For the gene normalization task, a string matching technique that can be quickly applied to new domains was shown to perform close to average.
The technologies being developed were shown to be readily adapted to the BioCreative II tasks. Although high performance may be obtained on individual tasks such as gene mention recognition and normalization, and document classification, tasks in which a number of components must be combined, such as detection and normalization of interacting protein pairs, are still challenging for NLP systems.
The immense corpus of biomedical literature existing today poses challenges in information search and integration. Many links between pieces of knowledge occur or are significant only under certain contexts—rather than under the entire corpus. This study proposes using networks of ontology concepts, linked based on their co-occurrences in annotations of abstracts of biomedical literature and descriptions of experiments, to draw conclusions based on context-specific queries and to better integrate existing knowledge. In particular, a Bayesian network framework is constructed to allow for the linking of related terms from two biomedical ontologies under the queried context concept. Edges in such a Bayesian network allow associations between biomedical concepts to be quantified and inference to be made about the existence of some concepts given prior information about others. This approach could potentially be a powerful inferential tool for context-specific queries, applicable to ontologies in other fields as well.
While search engines have become nearly ubiquitous on the Web, electronic health records (EHRs) generally lack search functionality; furthermore, there is no knowledge on how and what healthcare providers search while using an EHR-based search utility. In this study, we sought to understand user needs as captured by their search queries.
This post-implementation study analyzed user search log files for 6 months from an EHR-based, free-text search utility at our large academic institution. The search logs were de-identified and then analyzed in two steps. First, two investigators classified all the unique queries as navigational, transactional, or informational searches. Second, three physician reviewers categorized a random sample of 357 informational searches into high-level semantic types derived from the Unified Medical Language System (UMLS). The reviewers were given overlapping data sets, such that two physicians reviewed each query.
We analyzed 2,207 queries performed by 436 unique users over a 6-month period. Of the 2,207 queries, 980 were unique queries. Users of the search utility included clinicians, researchers and administrative staff. Across the whole user population, approximately 14.5% of the user searches were navigational searches and 85.1% were informational. Within informational searches, we found that users predominantly searched for laboratory results and specific diseases.
A variety of user types, ranging from clinicians to administrative staff, took advantage of the EHR-based search utility. Though these users' search behavior differed, they predominantly performed informational searches related to laboratory results and specific diseases. Additionally, a number of queries were part of words, implying the need for a free-text module to be included in any future concept-based search algorithm.
Information Storage and Retrieval; Medical Informatics; Medical Informatics Applications; Medical Records Systems; Computerized
A major challenge for functional and comparative genomics resource development is the extraction of data from the biomedical literature. Although text mining for biological data is an active research field, few applications have been integrated into production literature curation systems such as those of the model organism databases (MODs). Not only are most available biological natural language (bioNLP) and information retrieval and extraction solutions difficult to adapt to existing MOD curation workflows, but many also have high error rates or are unable to process documents available in those formats preferred by scientific journals.
In September 2008, Mouse Genome Informatics (MGI) at The Jackson Laboratory initiated a search for dictionary-based text mining tools that we could integrate into our biocuration workflow. MGI has rigorous document triage and annotation procedures designed to identify appropriate articles about mouse genetics and genome biology. We currently screen ∼1000 journal articles a month for Gene Ontology terms, gene mapping, gene expression, phenotype data and other key biological information. Although we do not foresee that curation tasks will ever be fully automated, we are eager to implement named entity recognition (NER) tools for gene tagging that can help streamline our curation workflow and simplify gene indexing tasks within the MGI system. Gene indexing is an MGI-specific curation function that involves identifying which mouse genes are being studied in an article, then associating the appropriate gene symbols with the article reference number in the MGI database.
Here, we discuss our search process, performance metrics and success criteria, and how we identified a short list of potential text mining tools for further evaluation. We provide an overview of our pilot projects with NCBO's Open Biomedical Annotator and Fraunhofer SCAI's ProMiner. In doing so, we prove the potential for the further incorporation of semi-automated processes into the curation of the biomedical literature.
PubMed is the most widely used tool for searching biomedical literature online. As with many other online search tools, a user often types a series of multiple related queries before retrieving satisfactory results to fulfill a single information need. Meanwhile, it is also a common phenomenon to see a user type queries on unrelated topics in a single session. In order to study PubMed users’ search strategies, it is necessary to be able to automatically separate unrelated queries and group together related queries. Here, we report a novel approach combining both lexical and contextual analyses for segmenting PubMed query sessions and identifying related queries and compare its performance with the previous approach based solely on concept mapping.
We experimented with our integrated approach on sample data consisting of 1,539 pairs of consecutive user queries in 351 user sessions. The prediction results of 1,396 pairs agreed with the gold-standard annotations, achieving an overall accuracy of 90.7%. This demonstrates that our approach is significantly better than the previously published method. By applying this approach to a one day query log of PubMed, we found that a significant proportion of information needs involved more than one PubMed query, and that most of the consecutive queries for the same information need are lexically related. Finally, the proposed PubMed distance is shown to be an accurate and meaningful measure for determining the contextual similarity between biological terms. The integrated approach can play a critical role in handling real-world PubMed query log data as is demonstrated in our experiments.
PubMed Distance; Related Query; PubMed Query Log; Session Segmentation; Lexical Similarity; Contextual Similarity
Most present day information retrieval systems use the presence or absence of certain words to decide which documents are appropriate for a user's query. This approach has had certain successes, but it fails to capture relationships between concepts represented by the words, and hence reduces the potential specificity of both indexing and searching of documents. A richer representation of the semantics of documents and queries, and methods for reasoning about these representations, have been provided by artificial intelligence. Navigational tools for browsing and authoring knowledge bases (KB's) add a convenient technique for focusing in the complex landscape of semantic representations. The center of such representations is usually a frame or a semantic network system. We are developing a prototype Unified Medical Language System (UMLS) taxonomy to represent objects and relationships in medicine. One focus of our research is improved methods for indexing and querying repositories of biomedical literature. The technique which we propose is based on the notion of relatedness of concepts. To this end we define heuristics which find related concepts and apply it to the UMLS taxonomy. Preliminary results from experiments with the implemented heuristics demonstrate its potential usefulness.
Tools to automatically summarize gene information from the literature have the potential to help genomics researchers better interpret gene expression data and investigate biological pathways. The task of finding information on sets of genes is common for genomic researchers, and PubMed is still the first choice because the most recent and original information can only be found in the unstructured, free text biomedical literature. However, finding information on a set of genes by manually searching and scanning the literature is a time-consuming and daunting task for scientists. We built and evaluated a query-based automatic summarizer of information on mouse genes studied in microarray experiments. The system clusters a set of genes by MeSH, GO and free text features and presents summaries for each gene by ranked sentences extracted from MEDLINE abstracts. Evaluation showed that the system seems to provide meaningful clusters and informative sentences are ranked higher by the algorithm.
Synthesis of data from published human genetic association studies is a critical step in the translation of human genome discoveries into health applications. Although genetic association studies account for a substantial proportion of the abstracts in PubMed, identifying them with standard queries is not always accurate or efficient. Further automating the literature-screening process can reduce the burden of a labor-intensive and time-consuming traditional literature search. The Support Vector Machine (SVM), a well-established machine learning technique, has been successful in classifying text, including biomedical literature. The GAPscreener, a free SVM-based software tool, can be used to assist in screening PubMed abstracts for human genetic association studies.
The data source for this research was the HuGE Navigator, formerly known as the HuGE Pub Lit database. Weighted SVM feature selection based on a keyword list obtained by the two-way z score method demonstrated the best screening performance, achieving 97.5% recall, 98.3% specificity and 31.9% precision in performance testing. Compared with the traditional screening process based on a complex PubMed query, the SVM tool reduced by about 90% the number of abstracts requiring individual review by the database curator. The tool also ascertained 47 articles that were missed by the traditional literature screening process during the 4-week test period. We examined the literature on genetic associations with preterm birth as an example. Compared with the traditional, manual process, the GAPscreener both reduced effort and improved accuracy.
GAPscreener is the first free SVM-based application available for screening the human genetic association literature in PubMed with high recall and specificity. The user-friendly graphical user interface makes this a practical, stand-alone application. The software can be downloaded at no charge.
The overwhelming amount of available scholarly literature in the life sciences poses significant challenges to scientists wishing to keep up with important developments related to their research, but also provides a useful resource for the discovery of recent information concerning genes, diseases, compounds and the interactions between them. In this paper, we describe an algorithm called Bio-LDA that uses extracted biological terminology to automatically identify latent topics, and provides a variety of measures to uncover putative relations among topics and bio-terms. Relationships identified using those approaches are combined with existing data in life science datasets to provide additional insight. Three case studies demonstrate the utility of the Bio-LDA model, including association predication, association search and connectivity map generation. This combined approach offers new opportunities for knowledge discovery in many areas of biology including target identification, lead hopping and drug repurposing.
The biomedical research community relies on a diverse set of resources, both within their own institutions and at other research centers. In addition, an increasing number of shared electronic resources have been developed. Without effective means to locate and query these resources, it is challenging, if not impossible, for investigators to be aware of the myriad resources available, or to effectively perform resource discovery when the need arises. In this paper, we describe the development and use of the Biomedical Resource Ontology (BRO) to enable semantic annotation and discovery of biomedical resources. We also describe the Resource Discovery System (RDS) which is a federated, inter-institutional pilot project that uses the BRO to facilitate resource discovery on the Internet. Through the RDS framework and its associated Biositemaps infrastructure, the BRO facilitates semantic search and discovery of biomedical resources, breaking down barriers and streamlining scientific research that will improve human health.
Ontology; Biositemaps; Resources; Biomedical research; Resource annotation; Resource discovery; Search; Semantic web; Web 2.0; Clinical and Translational Science Awards