U-Compare bio-event meta-service: compatible BioNLP event extraction services.

BACKGROUND: Bio-molecular event extraction from literature is recognized as an important task of bio text mining and, as such, many relevant systems have been developed and made available during the last decade. While such systems provide useful services individually, there is a need for a meta-service to enable comparison and ensemble of such services, offering optimal solutions for various purposes. RESULTS: We have integrated nine event extraction systems in the U-Compare framework, making them intercompatible and interoperable with other U-Compare components. The U-Compare event meta-service provides various meta-level features for comparison and ensemble of multiple event extraction systems. Experimental results show that the performance improvements achieved by the ensemble are significant. CONCLUSIONS: While individual event extraction systems themselves provide useful features for bio text mining, the U-Compare meta-service is expected to improve the accessibility to the individual systems, and to enable meta-level uses over multiple event extraction systems such as comparison and ensemble.

Mining of vaccine-associated IFN-γ gene interaction networks using the Vaccine Ontology.

BACKGROUND: Interferon-gamma (IFN-γ) is vital in vaccine-induced immune defense against bacterial and viral infections and tumor. Our recent study demonstrated the power of a literature-based discovery method in extraction and comparison of the IFN-γ and vaccine-mediated gene interaction networks. The Vaccine Ontology (VO) contains a hierarchy of vaccine names. It is hypothesized that the application of VO will enhance the prediction of IFN-γ and vaccine-mediated gene interaction network. RESULTS: In this study, 186 specific vaccine names listed in the Vaccine Ontology (VO) and their semantic relations were used for possible improved retrieval of the IFN-γ and vaccine associated gene interactions. The application of VO allows discovery of 38 more genes and 60 more interactions. Comparison of different layers of IFN-γ networks and the example BCG vaccine-induced subnetwork led to generation of new hypotheses. By analyzing all discovered genes using centrality metrics, 32 genes were ranked high in the VO-based IFN-γ vaccine network using four centrality scores. Furthermore, 28 specific vaccines were found to be associated with these top 32 genes. These specific vaccine-gene associations were further used to generate a network of vaccine-vaccine associations. The BCG and LVS vaccines are found to be the most central vaccines in the vaccine-vaccine association network. CONCLUSION: Our results demonstrate that the combined usages of biomedical ontologies and centrality-based literature mining are able to significantly facilitate discovery of gene interaction networks and gene-concept associations. AVAILABILITY: VO is available at: http://www.violinet.org/vaccineontology; and the SVM edit kernel for gene interaction extraction is available at: http://www.violinet.org/ifngvonet/int_ext_svm.zip.

Literature-based discovery of IFN-gamma and vaccine-mediated gene interaction networks.

Interferon-gamma (IFN-gamma) regulates various immune responses that are often critical for vaccine-induced protection. In order to annotate the IFN-gamma-related gene interaction network from a large amount of IFN-gamma research reported in the literature, a literature-based discovery approach was applied with a combination of natural language processing (NLP) and network centrality analysis. The interaction network of human IFN-gamma (Gene symbol: IFNG) and its vaccine-specific subnetwork were automatically extracted using abstracts from all articles in PubMed. Four network centrality metrics were further calculated to rank the genes in the constructed networks. The resulting generic IFNG network contains 1060 genes and 26313 interactions among these genes. The vaccine-specific subnetwork contains 102 genes and 154 interactions. Fifty six genes such as TNF, NFKB1, IL2, IL6, and MAPK8 were ranked among the top 25 by at least one of the centrality methods in one or both networks. Gene enrichment analysis indicated that these genes were classified in various immune mechanisms such as response to extracellular stimulus, lymphocyte activation, and regulation of apoptosis. Literature evidence was manually curated for the IFN-gamma relatedness of 56 genes and vaccine development relatedness for 52 genes. This study also generated many new hypotheses worth further experimental studies.

Introducing meta-services for biomedical information extraction.

We introduce the first meta-service for information extraction in molecular biology, the BioCreative MetaServer (BCMS; http://bcms.bioinfo.cnio.es/). This prototype platform is a joint effort of 13 research groups and provides automatically generated annotations for PubMed/Medline abstracts. Annotation types cover gene names, gene IDs, species, and protein-protein interactions. The annotations are distributed by the meta-server in both human and machine readable formats (HTML/XML). This service is intended to be used by biomedical researchers and database annotators, and in biomedical language processing. The platform allows direct comparison, unified access, and result aggregation of the annotations.

Identifying gene-disease associations using centrality on a literature mined gene-interaction network.

MOTIVATION: Understanding the role of genetics in diseases is one of the most important aims of the biological sciences. The completion of the Human Genome Project has led to a rapid increase in the number of publications in this area. However, the coverage of curated databases that provide information manually extracted from the literature is limited. Another challenge is that determining disease-related genes requires laborious experiments. Therefore, predicting good candidate genes before experimental analysis will save time and effort. We introduce an automatic approach based on text mining and network analysis to predict gene-disease associations. We collected an initial set of known disease-related genes and built an interaction network by automatic literature mining based on dependency parsing and support vector machines. Our hypothesis is that the central genes in this disease-specific network are likely to be related to the disease. We used the degree, eigenvector, betweenness and closeness centrality metrics to rank the genes in the network. RESULTS: The proposed approach can be used to extract known and to infer unknown gene-disease associations. We evaluated the approach for prostate cancer. Eigenvector and degree centrality achieved high accuracy. A total of 95% of the top 20 genes ranked by these methods are confirmed to be related to prostate cancer. On the other hand, betweenness and closeness centrality predicted more genes whose relation to the disease is currently unknown and are candidates for experimental study. AVAILABILITY: A web-based system for browsing the disease-specific gene-interaction networks is available at: http://gin.ncibi.org.