@conference {W05-07, title = {W05-07: Ontology-based literature mining of E. coli vaccine-associated gene interaction networks}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. While extensive E. coli vaccine research has been conducted, we are still unable to fully protect ourselves against E. coli infections. In this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains. The Interaction Network Ontology (INO) includes various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology- based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated gene interactions. Using vaccine-related abstracts, our study identified 11,350 sentences that contain 88 unique INO interaction types and at least two out of 1,781 unique E. coli genes. From this big network, a sub-network that contains 5 E. coli vaccine genes, 62 other E. coli genes, and 25 INO interaction types were also identified. A centrality analysis of these gene interaction networks identified top ranked E. coli genes and INO interaction types. Our INO hierarchical classification also provided an effective way to identify and study the relations and patterns among the 25 interaction types.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Junguk Hur and Arzucan Ozgur and Edison Ong and Yongqun He} } @conference {BP01, title = {BP01: Ignet: A centrality and INO-based web system for analyzing and visualizing literature-mined networks}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {
Ignet (Integrative Gene Network) is a web-based system for dynamical- ly updating and analyzing gene interaction networks mined using all Pub- Med abstracts. Four centrality metrics, namely degree, eigenvector, be- tweenness, and closeness are used to determine the importance of genes in the networks. Different gene interaction types between genes are classified using the Interaction Network Ontology (INO) that classifies interaction types in an ontological hierarchy along with individual keywords listed for each interaction type. An interactive user interface is designed to explore the interaction network as well as the centrality and ontology based net- work analysis. Availability: http://ignet.hegroup.org.
}, url = {http://ceur-ws.org/Vol-1747/BP01_ICBO2016.pdf}, author = {Arzucan Ozgur and Junguk Hur and Zuoshuang Xiang and Edison Ong and Dragomir Radev and Yongqun He} }