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

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 archive...

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Detalles Bibliográficos
Autores principales: Bell, Lindsey, Chowdhary, Rajesh, Liu, Jun S., Niu, Xufeng, Zhang, Jinfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3124513/
https://www.ncbi.nlm.nih.gov/pubmed/21738677
http://dx.doi.org/10.1371/journal.pone.0021474
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author Bell, Lindsey
Chowdhary, Rajesh
Liu, Jun S.
Niu, Xufeng
Zhang, Jinfeng
author_facet Bell, Lindsey
Chowdhary, Rajesh
Liu, Jun S.
Niu, Xufeng
Zhang, Jinfeng
author_sort Bell, Lindsey
collection PubMed
description 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.
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spelling pubmed-31245132011-07-07 Integrated Bio-Entity Network: A System for Biological Knowledge Discovery Bell, Lindsey Chowdhary, Rajesh Liu, Jun S. Niu, Xufeng Zhang, Jinfeng PLoS One Research Article 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. Public Library of Science 2011-06-27 /pmc/articles/PMC3124513/ /pubmed/21738677 http://dx.doi.org/10.1371/journal.pone.0021474 Text en Bell et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Bell, Lindsey
Chowdhary, Rajesh
Liu, Jun S.
Niu, Xufeng
Zhang, Jinfeng
Integrated Bio-Entity Network: A System for Biological Knowledge Discovery
title Integrated Bio-Entity Network: A System for Biological Knowledge Discovery
title_full Integrated Bio-Entity Network: A System for Biological Knowledge Discovery
title_fullStr Integrated Bio-Entity Network: A System for Biological Knowledge Discovery
title_full_unstemmed Integrated Bio-Entity Network: A System for Biological Knowledge Discovery
title_short Integrated Bio-Entity Network: A System for Biological Knowledge Discovery
title_sort integrated bio-entity network: a system for biological knowledge discovery
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3124513/
https://www.ncbi.nlm.nih.gov/pubmed/21738677
http://dx.doi.org/10.1371/journal.pone.0021474
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