A mouse protein interactome through combined literature mining with multiple sources of interaction evidence
A mouse protein interactome through combined literature mining with multiple sources of interaction evidence
复制标题
通过文献挖掘与多种相互作用证据来源相结合的小鼠蛋白质相互作用组
DOI：
10.1007/s00726-009-0335-7
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发表时间：
2010-04-01
期刊：
影响因子：
3.5
通讯作者：
Zhang, Yizheng
中科院分区：
文献类型：
--
作者：
Li, Xiao;Cai, Haoyang;Zhang, Yizheng
蛋白质-蛋白质相互作用（PPI）在许多生物过程中起着至关重要的作用。近年来，蛋白质相互作用网络（PIN）的几个模式生物和人类已经产生，但很少有大规模的研究，对小鼠进行了实验或计算。在这项工作中，我们致力于绘制小鼠PIN，其中蛋白质相互作用隐藏在大量的生物医学文献中。在基于共现的文本挖掘方法之后，通过整合来自基因组和蛋白质组数据集的异质性证据，使用概率模型朴素贝叶斯来过滤假阳性相互作用。进一步使用支持向量机算法来选择具有物理相互作用的蛋白质对。通过与目前可用的几种模式生物和人类的PPI数据集进行比较，结果表明，推导出的小鼠PIN在全局水平上具有相似的拓扑特性，但局部差异较大。小鼠蛋白质相互作用数据集存储在小鼠蛋白质-蛋白质相互作用数据库（MppDB）中，该数据库是用于系统级理解哺乳动物中基因功能和生物过程的有用信息来源。访问MppDB数据库是公开的，可在http://bio.scu.edu.cn/mppi上访问。
Protein-protein interactions (PPIs) play crucial roles in a number of biological processes. Recently, protein interaction networks (PINs) for several model organisms and humans have been generated, but few large-scale researches for mice have ever been made neither experimentally nor computationally. In the work, we undertook an effort to map a mouse PIN, in which protein interactions are hidden in enormous amount of biomedical literatures. Following a co-occurrence-based text-mining approach, a probabilistic model-na < ve Bayesian was used to filter false-positive interactions by integrating heterogeneous kinds of evidence from genomic and proteomic datasets. A support vector machine algorithm was further used to choose protein pairs with physical interactions. By comparing with the currently available PPI datasets from several model organisms and humans, it showed that the derived mouse PINs have similar topological properties at the global level, but a high local divergence. The mouse protein interaction dataset is stored in the Mouse protein-protein interaction DataBase (MppDB) that is useful source of information for system-level understanding of gene function and biological processes in mammals. Access to the MppDB database is public available at http://bio.scu.edu.cn/mppi.