W05-07: Ontology-based literature mining of E. coli vaccine-associated gene interaction networks
|Title||W05-07: Ontology-based literature mining of E. coli vaccine-associated gene interaction networks|
|Publication Type||Conference Paper|
|Year of Publication||2016|
|Authors||Hur J, Ozgur A, Ong E, He Y|
|Conference Name||International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)|
|Publisher||CEUR-ws.org Volume 1747|
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.