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Front Microbiol. 2015 Dec 9;6:1386. doi: 10.3389/fmicb.2015.01386. eCollection 2015.

Literature Mining and Ontology based Analysis of Host-Brucella Gene-Gene Interaction Network.

Author information

1
Department of Computer Engineering, Boğaziçi University Istanbul, Turkey.
2
Department of Basic Sciences, School of Medicine and Health Sciences, University of North Dakota, Grand Forks ND, USA.
3
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.

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.

KEYWORDS:

Brucella; Interaction Network Ontology (INO); SciMiner; host and pathogen gene name recognition; host–pathogen interaction extraction; support vector machines (SVM); text mining

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