title: Literature Mining and Ontology based Analysis of Host-Brucella Gene-Gene Interaction Network.
abstract: Brucella is an intracellular bacterium that causes chronic brucellosis in humans and various mammals. The identification of host-Brucella interaction is crucial to understand host immunity against Brucella infection and Brucella pathogenesis against host immune responses. Most of the information about the inter-species interactions between host and Brucella genes is only available in the text of the scientific publications. Many text-mining systems for extracting gene and protein interactions have been proposed. However, only a few of them have been designed by considering the peculiarities of host-pathogen interactions. In this paper, we used a text mining approach for extracting host-Brucella gene-gene interactions from the abstracts of articles in PubMed. The gene-gene interactions here represent the interactions between genes and/or gene products (e.g., proteins). The SciMiner tool, originally designed for detecting mammalian gene/protein names in text, was extended to identify host and Brucella gene/protein names in the abstracts. Next, sentence-level and abstract-level co-occurrence based approaches, as well as sentence-level machine learning based methods, originally designed for extracting intra-species gene interactions, were utilized to extract the interactions among the identified host and Brucella genes. The extracted interactions were manually evaluated. A total of 46 host-Brucella gene interactions were identified and represented as an interaction network. Twenty four of these interactions were identified from sentence-level processing. Twenty two additional interactions were identified when abstract-level processing was performed. The Interaction Network Ontology (INO) was used to represent the identified interaction types at a hierarchical ontology structure. Ontological modeling of specific gene-gene interactions demonstrates that host-pathogen gene-gene interactions occur at experimental conditions which can be ontologically represented. Our results show that the introduced literature mining and ontology-based modeling approach are effective in retrieving and analyzing host-pathogen gene-gene interaction networks.
authors.affiliation: Department of Computer Engineering, Boğaziçi University Istanbul, Turkey.
authors.forename: İlknur
authors.initials: İ
authors.lastname: Karadeniz
authors.affiliation: Department of Basic Sciences, School of Medicine and Health Sciences, University of North Dakota, Grand Forks ND, USA.
authors.forename: Junguk
authors.initials: J
authors.lastname: Hur
authors.affiliation: Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, University of Michigan, Ann Arbor MI, USA ; Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor MI, USA ; Comprehensive Cancer Center, University of Michigan Health System, Ann Arbor MI, USA.
authors.forename: Yongqun
authors.initials: Y
authors.lastname: He
authors.affiliation: Department of Computer Engineering, Boğaziçi University Istanbul, Turkey.
authors.forename: Arzucan
authors.initials: A
authors.lastname: Özgür
keywords: Brucella; Interaction Network Ontology (INO); SciMiner; host and pathogen gene name recognition; host–pathogen interaction extraction; support vector machines (SVM); text mining