Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

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Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

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Title: Integrated Bio-Entity Network: A System for Biological Knowledge Discovery
Author: Bell, Lindsey; Chowdhary, Rajesh; Liu, Jun; Niu, Xufeng; Zhang, Jinfeng

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Citation: Bell, Lindsey, Rajesh Chowdhary, Jun S. Liu, Xufeng Niu, and Jinfeng Zhang. 2011. “Integrated Bio-Entity Network: A System for Biological Knowledge Discovery.” Edited by Ying Xu. PLoS ONE 6 (6) (June 27): e21474. doi:10.1371/journal.pone.0021474.
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Abstract: A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs.
Published Version: doi:10.1371/journal.pone.0021474
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:27002572
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