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However, with extensive\n                      <jats:italic>E. coli<\/jats:italic>\n                      vaccine research, we are still unable to fully protect ourselves against\n                      <jats:italic>E. coli<\/jats:italic>\n                      infections. To more rational development of effective and safe\n                      <jats:italic>E. coli<\/jats:italic>\n                      vaccine, it is important to better understand\n                      <jats:italic>E. coli<\/jats:italic>\n                      vaccine-associated gene interaction networks.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      In this study, we first extended the Vaccine Ontology (VO) to semantically represent various\n                      <jats:italic>E. coli<\/jats:italic>\n                      vaccines and genes used in the vaccine development. We also normalized\n                      <jats:italic>E. coli<\/jats:italic>\n                      gene names compiled from the annotations of various\n                      <jats:italic>E. coli<\/jats:italic>\n                      strains using a pan-genome-based annotation strategy. The Interaction Network Ontology (INO) includes a hierarchy of various interaction-related keywords useful for literature mining. Using VO, INO, and normalized\n                      <jats:italic>E. coli<\/jats:italic>\n                      gene names, we applied an ontology-based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve\n                      <jats:italic>E. coli<\/jats:italic>\n                      vaccine-associated\n                      <jats:italic>E. coli<\/jats:italic>\n                      gene interactions. Four centrality metrics (i.e., degree, eigenvector, closeness, and betweenness) were calculated for identifying highly ranked genes and interaction types.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Using vaccine-related PubMed abstracts, our study identified 11,350 sentences that contain 88 unique INO interactions types and 1,781 unique\n                      <jats:italic>E. coli<\/jats:italic>\n                      genes. Each sentence contained at least one interaction type and two unique\n                      <jats:italic>E. coli<\/jats:italic>\n                      genes. An\n                      <jats:italic>E. coli<\/jats:italic>\n                      gene interaction network of genes and INO interaction types was created. From this big network, a sub-network consisting of 5\n                      <jats:italic>E. coli<\/jats:italic>\n                      vaccine genes, including\n                      <jats:italic>carA<\/jats:italic>\n                      ,\n                      <jats:italic>carB<\/jats:italic>\n                      ,\n                      <jats:italic>fimH<\/jats:italic>\n                      ,\n                      <jats:italic>fepA<\/jats:italic>\n                      , and\n                      <jats:italic>vat<\/jats:italic>\n                      , and 62 other\n                      <jats:italic>E. coli<\/jats:italic>\n                      genes, and 25 INO interaction types was identified. While many interaction types represent direct interactions between two indicated genes, our study has also shown that many of these retrieved interaction types are indirect in that the two genes participated in the specified interaction process in a required but indirect process. Our centrality analysis of these gene interaction networks identified top ranked\n                      <jats:italic>E. coli<\/jats:italic>\n                      genes and 6 INO interaction types (e.g., regulation and gene expression).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Vaccine-related\n                      <jats:italic>E. coli<\/jats:italic>\n                      gene-gene interaction network was constructed using ontology-based literature mining strategy, which identified important\n                      <jats:italic>E. coli<\/jats:italic>\n                      vaccine genes and their interactions with other genes through specific interaction types.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13326-017-0122-4","type":"journal-article","created":{"date-parts":[[2017,3,13]],"date-time":"2017-03-13T23:10:31Z","timestamp":1489446631000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Ontology-based literature mining of E. coli vaccine-associated gene interaction networks"],"prefix":"10.1186","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0736-2149","authenticated-orcid":false,"given":"Junguk","family":"Hur","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arzucan","family":"\u00d6zg\u00fcr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongqun","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2017,3,14]]},"reference":[{"issue":"2","key":"122_CR1","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1038\/nrmicro818","volume":"2","author":"JB Kaper","year":"2004","unstructured":"Kaper JB, Nataro JP, Mobley HL. 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