http://www.jbiomedsem.com The latest research articles published by Journal of Biomedical Semantics 2012-04-01T00:00:00Z This is an RSS newsfeed from BioMed Central It is intended to be used with an RSS reader. For more information about RSS newsfeeds from BioMed Central, visit http://www.biomedcentral.com/info/about/rss/ Background: The wide variety of morphological variants of domain-specific technical terms contributes to the complexity of performing natural language processing of the scientific literature related to molecular biology. For morphological analysis of these texts, lemmatization has been actively applied in the recent biomedical research. Results: In this work, we developed a domain-specific lemmatization tool, BioLemmatizer, for the morphological analysis of biomedical literature. The tool focuses on the inflectional morphology of English and is based on the general English lemmatization tool MorphAdorner. The BioLemmatizer is further tailored to the biological domain through incorporation of several published lexical resources. It retrieves lemmas based on the use of a word lexicon, and defines a set of rules that transform a word to a lemma if it is not encountered in the lexicon. An innovative aspect of the BioLemmatizer is the use of a hierarchical strategy for searching the lexicon, which enables the discovery of the correct lemma even if the input Part-of-Speech information is inaccurate. The BioLemmatizer achieves an accuracy of 97.5% in lemmatizing an evaluation set prepared from the CRAFT corpus, a collection of full-text biomedical articles, and an accuracy of 97.6% on the LLL05 corpus. The contribution of the BioLemmatizer to accuracy improvement of a practical information extraction task is further demonstrated when it is used as a component in a biomedical text mining system. Conclusions: The BioLemmatizer outperforms other tools when compared with eight existing lemmatizers. The BioLemmatizer is released as an open source software and can be downloaded from http://biolemmatizer.sourceforge.net. http://www.jbiomedsem.com/content/3/1/3 Haibin Liu Tom Christiansen William Baumgartner Karin Verspoor Journal of Biomedical Semantics 2012, null:3 2012-04-01T00:00:00Z doi:10.1186/2041-1480-3-3 Biomedical text mining enhanced by lemmatization tool Morphological analysis of biomedical literature is more effective when performed by a new open source lemmatization tool, BioLemmatizer, which also improves the accuracy of practical information extraction tasks, and outperforms other similar pre-existing software. /content/figures/2041-1480-3-3-toc.gif Journal of Biomedical Semantics 2041-1480 ${item.volume} 3 2012-04-01T00:00:00Z PDF Background: Rapid identification of subject experts for medical topics helps in improving the implementation of discoveries by speeding the time to market drugs and aiding in clinical trial recruitment, etc. Identifying such people who influence opinion through social network analysis is gaining prominence. In this work, we explore how to combine named entity recognition from unstructured news articles with social network analysis to discover opinion leaders for a given medical topic. Methods: We employed a Conditional Random Field algorithm to extract three categories of entities from health-related new articles: Person, Organization and Location. We used the latter two to disambiguate polysemy and synonymy for the person names, used simple rules to identify the subject experts, and then applied social network analysis techniques to discover the opinion leaders among them based on their media presence. A network was created by linking each pair of subject experts who are mentioned together in an article. The social network analysis metrics (including centrality metrics such as Betweenness, Closeness, Degree and Eigenvector) are used for ranking the subject experts based on their power in information flow. Results: We extracted 734,204 person mentions from 147,528 news articles related to obesity from January 1, 2007 through July 22, 2010. Of these, 147,879 mentions have been marked as subject experts. The F-score of extracting person names is 88.5%. More than 80% of the subject experts who rank among top 20 in at least one of the metrics could be considered as opinion leaders in obesity. Conclusion: The analysis of the network of subject experts with media presence revealed that an opinion leader might have fewer mentions in the news articles, but a high network centrality measure and vice-versa. Betweenness, Closeness and Degree centrality measures were shown to supplement frequency counts in the task of finding subject experts. Further, opinion leaders missed in scientific publication network analysis could be retrieved from news articles. http://www.jbiomedsem.com/content/3/1/2 Siddhartha Jonnalagadda Ryan Peeler Philip Topham Journal of Biomedical Semantics 2012, null:2 2012-03-15T00:00:00Z doi:10.1186/2041-1480-3-2 Identifying medical experts using newspaper articles An algorithm designed to recognize experts mentioned in news articles and social media allows medical researchers to identify opinion leaders in the field, potentially helping to implement new findings. /content/figures/2041-1480-3-2-toc.gif Journal of Biomedical Semantics 2041-1480 ${item.volume} 2 2012-03-15T00:00:00Z XML Background: The International Classification of Functioning, Disability and Health (ICF) is a classification of health and health-related issues, aimed at describing and measuring health and disability at both individual and population levels. Here we discuss a preliminary qualitative and quantitative analysis of the relationships used in the Activities and Participation component of ICF, and a preliminary mapping to SUMO (Suggested Upper Merged Ontology) concepts. The aim of the analysis is to identify potential logical problems within this component of ICF, and to understand whether activities and participation might be defined more formally than in the current version of ICF. Results: In the relationship analysis, we used four predicates among those available in SUMO for processes (Patient, Instrument, Agent, and subProcess). While at the top level subsumption was used in most cases (90%), at the lower levels the percentage of other relationships rose to 41%. Chapters were heterogeneous in the relationships used and some of the leaves of the tree seemed to represent properties or parts of the parent concept rather than subclasses. Mapping of ICF to SUMO proved partially feasible, with the activity concepts being mapped mostly (but not totally) under the IntentionalProcess concept in SUMO. On the other hand, the participation concept has not been mapped to any upper level concept. Conclusions: Our analysis of the relationships within ICF revealed issues related to confusion between classes and their properties, incorrect classifications, and overemphasis on subsumption, confirming what already observed by other researchers. However, it also suggested some properties for Activities that could be included in a more formal model: number of agents involved, the instrument used to carry out the activity, the object of the activity, complexity of the task, and an enumeration of relevant subtasks. http://www.jbiomedsem.com/content/3/1/1 Vincenzo Della Mea Andrea Simoncello Journal of Biomedical Semantics 2012, null:1 2012-02-28T00:00:00Z doi:10.1186/2041-1480-3-1 Ontological analysis of ICF proposes better classifications An ontology-based exploration of the International Classification of Functioning, Disability and Health (ICF) proposes more accurate classifications of components within this tool, which could lead to the development of a more efficient model. /content/figures/2041-1480-3-1-toc.gif Journal of Biomedical Semantics 2041-1480 ${item.volume} 1 2012-02-28T00:00:00Z XML Background: Caused by intracellular Gram-negative bacteria Brucella spp., brucellosis is the most common bacterial zoonotic disease. Extensive studies in brucellosis have yielded a large number of publications and data covering various topics ranging from basic Brucella genetic study to vaccine clinical trials. To support data interoperability and reasoning, a community-based brucellosis-specific biomedical ontology is needed. Results: The Brucellosis Ontology (IDOBRU: http://sourceforge.net/projects/idobru), a biomedical ontology in the brucellosis domain, is an extension ontology of the core Infectious Disease Ontology (IDO-core) and follows OBO Foundry principles. Currently IDOBRU contains 1503 ontology terms, which includes 739 Brucella-specific terms, 414 IDO-core terms, and 350 terms imported from 10 existing ontologies. IDOBRU has been used to model different aspects of brucellosis, including host infection, zoonotic disease transmission, symptoms, virulence factors and pathogenesis, diagnosis, intentional release, vaccine prevention, and treatment. Case studies are typically used in our IDOBRU modeling. For example, diurnal temperature variation in Brucella patients, a Brucella-specific PCR method, and a WHO-recommended brucellosis treatment were selected as use cases to model brucellosis symptom, diagnosis, and treatment, respectively. Developed using OWL, IDOBRU supports OWL-based ontological reasoning. For example, by performing a Description Logic (DL) query in the OWL editor Protégé 4 or a SPARQL query in an IDOBRU SPARQL server, a check of Brucella virulence factors showed that eight of them are known protective antigens based on the biological knowledge captured within the ontology. Conclusions: IDOBRU is the first reported bacterial infectious disease ontology developed to represent different disease aspects in a formal logical format. It serves as a brucellosis knowledgebase and supports brucellosis data integration and automated reasoning. http://www.jbiomedsem.com/content/2/1/9 Yu Lin Zuoshuang Xiang Yongqun He Journal of Biomedical Semantics 2011, null:9 2011-10-31T00:00:00Z doi:10.1186/2041-1480-2-9 Brucellosis Ontology The first reported bacterial infectious disease ontology, that provides a brucellosis knowledge-base and supports brucellosis data integration and automated reasoning. /content/figures/2041-1480-2-9-toc.gif Journal of Biomedical Semantics 2041-1480 ${item.volume} 9 2011-10-31T00:00:00Z XML Background: The complexity and inter-related nature of biological data poses a difficult challenge for data and tool integration. There has been a proliferation of interoperability standards and projects over the past decade, none of which has been widely adopted by the bioinformatics community. Recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community.DescriptionSADI - Semantic Automated Discovery and Integration - is a lightweight set of fully standards-compliant Semantic Web service design patterns that simplify the publication of services of the type commonly found in bioinformatics and other scientific domains. Using Semantic Web technologies at every level of the Web services "stack", SADI services consume and produce instances of OWL Classes following a small number of very straightforward best-practices. In addition, we provide codebases that support these best-practices, and plug-in tools to popular developer and client software that dramatically simplify deployment of services by providers, and the discovery and utilization of those services by their consumers. Conclusions: SADI Services are fully compliant with, and utilize only foundational Web standards; are simple to create and maintain for service providers; and can be discovered and utilized in a very intuitive way by biologist end-users. In addition, the SADI design patterns significantly improve the ability of software to automatically discover appropriate services based on user-needs, and automatically chain these into complex analytical workflows. We show that, when resources are exposed through SADI, data compliant with a given ontological model can be automatically gathered, or generated, from these distributed, non-coordinating resources - a behaviour we have not observed in any other Semantic system. Finally, we show that, using SADI, data dynamically generated from Web services can be explored in a manner very similar to data housed in static triple-stores, thus facilitating the intersection of Web services and Semantic Web technologies. http://www.jbiomedsem.com/content/2/1/8 Mark Wilkinson Benjamin Vandervalk Luke McCarthy Journal of Biomedical Semantics 2011, null:8 2011-10-24T00:00:00Z doi:10.1186/2041-1480-2-8 /content/figures/2041-1480-2-8-toc.gif Journal of Biomedical Semantics 2041-1480 ${item.volume} 8 2011-10-24T00:00:00Z XML Background: In this paper we describe our experience of adding globally unique identifiers to the Species 2000 and ITIS Catalogue of Life, an on-line index of organisms which is intended, ultimately, to cover all the world's known species. The scientific species names held in the Catalogue are names that already play an extensive role as terms in the organisation of information about living organisms in bioinformatics and other domains, but the effectiveness of their use is hindered by variation in individuals' opinions and understanding of these terms; indeed, in some cases more than one name will have been used to refer to the same organism. This means that it is desirable to be able to give unique labels to each of these differing concepts within the catalogue and to be able to determine which concepts are being used in other systems, in order that they can be associated with the concepts in the catalogue. Not only is this needed, but it is also necessary to know the relationships between alternative concepts that scientists might have employed, as these determine what can be inferred when data associated with related concepts is being processed. A further complication is that the catalogue itself is evolving as scientific opinion changes due to an increasing understanding of life. Results: We describe how we are using Life Science Identifiers (LSIDs) as globally unique identifiers in the Catalogue of Life, explaining how the mapping to species concepts is performed, how concepts are associated with specific editions of the catalogue, and how the Taxon Concept Schema has been adopted in order to express information about concepts and their relationships. We explore the implications of using globally unique identifiers in order to refer to abstract concepts such as species, which incorporate at least a measure of subjectivity in their definition, in contrast with the more traditional use of such identifiers to refer to more tangible entities, events, documents, observations, etc. Conclusions: A major reason for adopting identifiers such as LSIDs is to facilitate data integration. We have demonstrated the incorporation of LSIDs into the Catalogue of Life, in a manner consistent with the biodiversity informatics community's conventions for LSID use. The Catalogue of Life is therefore available as a taxonomy of organisms for use within various disciplines, including biomedical research, by software written with an awareness of these conventions. http://www.jbiomedsem.com/content/2/1/7 Andrew Jones Richard White Ewen Orme Journal of Biomedical Semantics 2011, null:7 2011-10-17T00:00:00Z doi:10.1186/2041-1480-2-7 Life Science Identifiers facilitate data integration The challenge of managing the variability of species names can be addressed with globally unique Life Science Identifiers (LSIDs) to map species names to abstract concepts, which should assist in data integration and interoperability in biomedicine. /content/figures/2041-1480-2-7-toc.gif Journal of Biomedical Semantics 2041-1480 ${item.volume} 7 2011-10-17T00:00:00Z XML Background: Ontologies are increasingly used to structure and semantically describe entities of domains, such as genes and proteins in life sciences. Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data. Results: We present GOMMA, a generic infrastructure for managing and analyzing life science ontologies and their evolution. GOMMA utilizes a generic repository to uniformly and efficiently manage ontology versions and different kinds of mappings. Furthermore, it provides components for ontology matching, and determining evolutionary ontology changes. These components are used by analysis tools, such as the Ontology Evolution Explorer (OnEX) and the detection of unstable ontology regions. We introduce the component-based infrastructure and show analysis results for selected components and life science applications. GOMMA is available at http://dbs.uni-leipzig.de/GOMMA. Conclusions: GOMMA provides a comprehensive and scalable infrastructure to manage large life science ontologies and analyze their evolution. Key functions include a generic storage of ontology versions and mappings, support for ontology matching and determining ontology changes. The supported features for analyzing ontology changes are helpful to assess their impact on ontology-dependent applications such as for term enrichment. GOMMA complements OnEX by providing functionalities to manage various versions of mappings between two ontologies and allows combining different match approaches. http://www.jbiomedsem.com/content/2/1/6 Toralf Kirsten Anika Gross Michael Hartung Erhard Rahm Journal of Biomedical Semantics 2011, null:6 2011-09-13T00:00:00Z doi:10.1186/2041-1480-2-6 /content/figures/2041-1480-2-6-toc.gif Journal of Biomedical Semantics 2041-1480 ${item.volume} 6 2011-09-13T00:00:00Z XML Background: The large-scale effort in developing, maintaining and making biomedical ontologies available motivates the application of similarity measures to compare ontology concepts or, by extension, the entities described therein. A common approach, known as semantic similarity, compares ontology concepts through the information content they share in the ontology. However, different disjunctive ancestors in the ontology are frequently neglected, or not properly explored, by semantic similarity measures. Results: This paper proposes a novel method, dubbed DiShIn, that effectively exploits the multiple inheritance relationships present in many biomedical ontologies. DiShIn calculates the shared information content of two ontology concepts, based on the information content of the disjunctive common ancestors of the concepts being compared. DiShIn identifies these disjunctive ancestors through the number of distinct paths from the concepts to their common ancestors. Conclusions: DiShIn was applied to Gene Ontology and its performance was evaluated against state-of-the-art measures using CESSM, a publicly available evaluation platform of protein similarity measures. By modifying the way traditional semantic similarity measures calculate the shared information content, DiShIn was able to obtain a statistically significant higher correlation between semantic and sequence similarity. Moreover, the incorporation of DiShIn in existing applications that exploit multiple inheritance would reduce their execution time. http://www.jbiomedsem.com/content/2/1/5 Francisco Couto Mario Silva Journal of Biomedical Semantics 2011, null:5 2011-08-31T00:00:00Z doi:10.1186/2041-1480-2-5 /content/figures/2041-1480-2-5-toc.gif Journal of Biomedical Semantics 2041-1480 ${item.volume} 5 2011-08-31T00:00:00Z XML Background: The interaction between biological researchers and the bioinformatics tools they use is still hampered by incomplete interoperability between such tools. To ensure interoperability initiatives are effectively deployed, end-user applications need to be aware of, and support, best practices and standards. Here, we report on an initiative in which software developers and genome biologists came together to explore and raise awareness of these issues: BioHackathon 2009. Results: Developers in attendance came from diverse backgrounds, with experts in Web services, workflow tools, text mining and visualization. Genome biologists provided expertise and exemplar data from the domains of sequence and pathway analysis and glyco-informatics. One goal of the meeting was to evaluate the ability to address real world use cases in these domains using the tools that the developers represented. This resulted in i) a workflow to annotate 100,000 sequences from an invertebrate species; ii) an integrated system for analysis of the transcription factor binding sites (TFBSs) enriched based on differential gene expression data obtained from a microarray experiment; iii) a workflow to enumerate putative physical protein interactions among enzymes in a metabolic pathway using protein structure data; iv) a workflow to analyze glyco-gene-related diseases by searching for human homologs of glyco-genes in other species, such as fruit flies, and retrieving their phenotype-annotated SNPs. Conclusions: Beyond deriving prototype solutions for each use-case, a second major purpose of the BioHackathon was to highlight areas of insufficiency. We discuss the issues raised by our exploration of the problem/solution space, concluding that there are still problems with the way Web services are modeled and annotated, including: i) the absence of several useful data or analysis functions in the Web service "space"; ii) the lack of documentation of methods; iii) lack of compliance with the SOAP/WSDL specification among and between various programming-language libraries; and iv) incompatibility between various bioinformatics data formats. Although it was still difficult to solve real world problems posed to the developers by the biological researchers in attendance because of these problems, we note the promise of addressing these issues within a semantic framework. http://www.jbiomedsem.com/content/2/1/4 Toshiaki Katayama Mark Wilkinson Rutger Vos Takeshi Kawashima Shuichi Kawashima Mitsuteru Nakao Yasunori Yamamoto Hong-Woo Chun Atsuko Yamaguchi Shin Kawano Jan Aerts Kiyoko Aoki-Kinoshita Kazuharu Arakawa Bruno Aranda Raoul Bonnal Jose Fernandez Takatomo Fujisawa Paul Gordon Naohisa Goto Syed Haider Todd Harris Takashi Hatakeyama Isaac Ho Masumi Itoh Arek Kasprzyk Nobuhiro Kido Young-Joo Kim Akira Kinjo Fumikazu Konishi Yulia Kovarskaya Greg von Kuster Alberto Labarga Vachiranee Limviphuvadh Luke McCarthy Yasukazu Nakamura Yunsun Nam Kozo Nishida Kunihiro Nishimura Tatsuya Nishizawa Soichi Ogishima Tom Oinn Shinobu Okamoto Shujiro Okuda Keiichiro Ono Kazuki Oshita Keun-Joon Park Nicholas Putnam Martin Senger Jessica Severin Yasumasa Shigemoto Hideaki Sugawara James Taylor Oswaldo Trelles Chisato Yamasaki Riu Yamashita Noriyuki Satoh Toshihisa Takagi Journal of Biomedical Semantics 2011, null:4 2011-08-02T00:00:00Z doi:10.1186/2041-1480-2-4 Towards interoperable bioinformatics Web services The challenges discussed during the second DBCLS BioHackathon, from data providers, middleware providers, interface designers and end-users perspective. /content/figures/2041-1480-2-4-toc.gif Journal of Biomedical Semantics 2041-1480 ${item.volume} 4 2011-08-02T00:00:00Z XML Background: Existing methods for calculating semantic similarity between gene products using the Gene Ontology (GO) often rely on external resources, which are not part of the ontology. Consequently, changes in these external resources like biased term distribution caused by shifting of hot research topics, will affect the calculation of semantic similarity. One way to avoid this problem is to use semantic methods that are "intrinsic" to the ontology, i.e. independent of external knowledge. Results: We present a shortest-path graph kernel (spgk) method that relies exclusively on the GO and its structure. In spgk, a gene product is represented by an induced subgraph of the GO, which consists of all the GO terms annotating it. Then a shortest-path graph kernel is used to compute the similarity between two graphs. In a comprehensive evaluation using a benchmark dataset, spgk compares favorably with other methods that depend on external resources. Compared with simUI, a method that is also intrinsic to GO, spgk achieves slightly better results on the benchmark dataset. Statistical tests show that the improvement is significant when the resolution and EC similarity correlation coefficient are used to measure the performance, but is insignificant when the Pfam similarity correlation coefficient is used. Conclusions: Spgk uses a graph kernel method in polynomial time to exploit the structure of the GO to calculate semantic similarity between gene products. It provides an alternative to both methods that use external resources and "intrinsic" methods with comparable performance. http://www.jbiomedsem.com/content/2/1/3 Marco Alvarez Xiaojun Qi Changhui Yan Journal of Biomedical Semantics 2011, null:3 2011-07-29T00:00:00Z doi:10.1186/2041-1480-2-3 /content/figures/2041-1480-2-3-toc.gif Journal of Biomedical Semantics 2041-1480 ${item.volume} 3 2011-07-29T00:00:00Z XML