Integrated bio-entity network: a system for biological knowledge discovery.
Integrated bio-entity network: a system for biological knowledge discovery.
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DOI：
10.1371/journal.pone.0021474
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Zhang J
中科院分区：
文献类型：
--
作者：
Bell L;Chowdhary R;Liu JS;Niu X;Zhang J
我们的生物学知识的一个重要部分集中在生物实体（bio-entities）之间的关系，例如蛋白质、基因、小分子、通路、基因本体（GO）术语和疾病。生物实体关系信息以越来越快的速度积累，以不同的形式归档在分散的地方。大多数此类信息都以非结构化文本形式隐藏在科学文献中。以结构化形式组织异构信息不仅有利于使用综合方法研究生物系统，而且还允许以自动和系统的方式发现新知识。在这项研究中，我们对来自两个包含手动注释的结构化信息的数据库和科学文献中非结构化文本的自动信息提取的生物实体关系信息进行了大规模集成。我们在本研究中整合的关系信息包括蛋白质-蛋白质相互作用、蛋白质/基因调控、蛋白质-小分子相互作用、蛋白质-GO关系、蛋白质-通路关系以及通路-疾病关系。关系信息被组织在图数据结构中，称为集成生物实体网络（IBN），其中顶点是生物实体，边代表它们的关系。在此框架下，可以设计图论算法来执行各种知识发现任务。我们设计了广度优先搜索剪枝（BFSP）和最可能路径（MPP）算法来自动生成假设 - 网络中具有高概率的间接关系。我们证明 IBN 可用于生成合理的假设，这不仅有助于更好地理解生物系统中复杂的相互作用，而且还为实验设计提供指导。
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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影响因子：
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作者：
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