http://www.jbiomedsem.com The latest research articles published by Journal of Biomedical Semantics 2011-10-31T00:00:00Z 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: 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 Background: The HL7 Clinical Document Architecture (CDA) constrains the HL7 Reference Information model (RIM) to specify the format of HL7-compliant clinical documents, dubbed CDA documents. The use of clinical terminologies such as SNOMED CT® further improves interoperability as they provide a shared understanding of concepts used in clinical documents. However, despite the use of the RIM and of shared terminologies such as SNOMED CT®, gaps remain as to how to use both the RIM and SNOMED CT® in HL7 clinical documents. The HL7 implementation guide on Using SNOMED CT in HL7 Version 3 is an effort to close this gap. It is, however, a human-readable document that is not suited for automatic processing. As such, health care professionals designing clinical documents need to ensure validity of documents manually. Results: We represent the CDA using the Ontology Web Language OWL and further use the OWL version of SNOMED CT® to enable the translation of CDA documents to so-called OWL ontologies. We formalize a subset of the constraints in the implementation guide on Using SNOMED CT in HL7 Version 3 as OWL Integrity Constraints and show that we can automatically validate CDA documents using OWL reasoners such as Pellet. Finally, we evaluate our approach via a prototype implementation that plugs in the Open Health Workbench. Conclusions: We present a methodology to automatically check the validity of CDA documents which make reference to SNOMED CT® terminology. The methodology relies on semantic technologies such as OWL. As such it removes the burden from IT health care professionals of having to manually implement such guidelines in systems that use HL7 Version 3 documents. http://www.jbiomedsem.com/content/2/1/2 Stijn Heymans Matthew McKennirey Joshua Phillips Journal of Biomedical Semantics 2011, null:2 2011-07-15T00:00:00Z doi:10.1186/2041-1480-2-2 /content/figures/2041-1480-2-2-toc.gif Journal of Biomedical Semantics 2041-1480 ${item.volume} 2 2011-07-15T00:00:00Z XML Background: Detection of sentences that describe protein-protein interactions (PPIs) in biomedical publications is a challenging and unresolved pattern recognition problem. Many state-of-the-art approaches for this task employ kernel classification methods, in particular support vector machines (SVMs). In this work we propose a novel data integration approach that utilises semantic kernels and a kernel classification method that is a probabilistic analogue to SVMs. Semantic kernels are created from statistical information gathered from large amounts of unlabelled text using lexical semantic models. Several semantic kernels are then fused into an overall composite classification space. In this initial study, we use simple features in order to examine whether the use of combinations of kernels constructed using word-based semantic models can improve PPI sentence detection. Results: We show that combinations of semantic kernels lead to statistically significant improvements in recognition rates and receiver operating characteristic (ROC) scores over the plain Gaussian kernel, when applied to a well-known labelled collection of abstracts. The proposed kernel composition method also allows us to automatically infer the most discriminative kernels. Conclusions: The results from this paper indicate that using semantic information from unlabelled text, and combinations of such information, can be valuable for classification of short texts such as PPI sentences. This study, however, is only a first step in evaluation of semantic kernels and probabilistic multiple kernel learning in the context of PPI detection. The method described herein is modular, and can be applied with a variety of feature types, kernels, and semantic models, in order to facilitate full extraction of interacting proteins. http://www.jbiomedsem.com/content/2/1/1 Tamara Polajnar Theodoros Damoulas Mark Girolami Journal of Biomedical Semantics 2011, null:1 2011-05-14T00:00:00Z doi:10.1186/2041-1480-2-1 /content/figures/2041-1480-2-1-toc.gif Journal of Biomedical Semantics 2041-1480 ${item.volume} 1 2011-05-14T00:00:00Z XML While classifications of mental disorders have existed for over one hundred years, it still remains unspecified what terms such as 'mental disorder', 'disease' and 'illness' might actually denote. While ontologies have been called in aid to address this shortfall since the GALEN project of the early 1990s, most attempts thus far have sought to provide a formal description of the structure of some pre-existing terminology or classification, rather than of the corresponding structures and processes on the side of the patient.We here present a view of mental disease that is based on ontological realism and which follows the principles embodied in Basic Formal Ontology (BFO) and in the application of BFO in the Ontology of General Medical Science (OGMS). We analyzed statements about what counts as a mental disease provided (1) in the research agenda for the DSM-V, and (2) in Pies' model. The results were used to assess whether the representational units of BFO and OGMS were adequate as foundations for a formal representation of the entities in reality that these statements attempt to describe. We then analyzed the representational units specific to mental disease and provided corresponding definitions.Our key contributions lie in the identification of confusions and conflations in the existing terminology of mental disease and in providing what we believe is a framework for the sort of clear and unambiguous reference to entities on the side of the patient that is needed in order to avoid these confusions in the future. http://www.jbiomedsem.com/content/1/1/10 Werner Ceusters Barry Smith Journal of Biomedical Semantics 2010, null:10 2010-12-09T00:00:00Z doi:10.1186/2041-1480-1-10 /content/figures/2041-1480-1-10-toc.gif Journal of Biomedical Semantics 2041-1480 ${item.volume} 10 2010-12-09T00:00:00Z XML