Efficiently mining protein interaction dependencies from large text corpora
Efficiently mining protein interaction dependencies from large text corpora
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DOI：
10.1039/c2ib00126h
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发表时间：
2012-01-01
影响因子：
2.5
通讯作者：
Rahmann, Sven
中科院分区：
文献类型：
--
作者：
Koester, Johannes;Zamir, Eli;Rahmann, Sven
生物化学研究已经获得了大量关于蛋白质相互作用之间依赖关系的信息，这些相互作用是由变构调节、位阻和其他机制产生的。总的来说，这些信息对于理解大的细胞内蛋白质网络是有价值的。然而，这些信息零星地分布在数以百万计的出版物中，并以供手工阅读的自由样式文本记录下来。在这里，我们开发了一种从大量出版物中提取交互依赖信息的计算方法。首先，基于关键字的标记化将全文减少为短字符串，促进了对可能指示交互依赖描述的模式的有效搜索。匹配这些模式的句子被提取出来，从而减少了人类管理员需要阅读的文本数量。将这种方法应用于从59933篇论文208篇简短陈述中提取的整合素粘附体网络，其中近一半确实描述了相互依赖关系。我们将获得的依赖关系的超网络可视化，并说明这些依赖关系限制了粘附位点组装的可行机制，并产生了关于其可切换性的可测试假设。
Biochemical research has yielded an extensive amount of information about dependencies between protein interactions, as generated by allosteric regulations, steric hindrance and other mechanisms. Collectively, this information is valuable for understanding large intracellular protein networks. However, this information is sparsely distributed among millions of publications and documented as freely styled text meant for manual reading. Here we develop a computational approach for extracting information about interaction dependencies from large numbers of publications. First, keyword-based tokenization reduces full papers to short strings, facilitating an efficient search for patterns that are likely to indicate descriptions of interaction dependencies. Sentences that match such patterns are extracted, thereby reducing the amount of text to be read by human curators. Application of this approach to the integrin adhesome network extracted from 59 933 papers 208 short statements, close to half of which indeed describe interaction dependencies. We visualize the obtained hypernetwork of dependencies and illustrate that these dependencies confine the feasible mechanisms of adhesion sites assembly and generate testable hypotheses about their switchability.