Mapping gene ontology to proteins based on protein-protein interaction data
Mapping gene ontology to proteins based on protein-protein interaction data
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DOI：
10.1093/bioinformatics/btg500
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发表时间：
2004-04-12
期刊：
影响因子：
5.8
通讯作者：
Chen, T
中科院分区：
文献类型：
--
作者：
Deng, MH;Tu, ZD;Chen, T
动机：基因本体（GO）联盟提供了蛋白质功能的结构描述，作为许多生物基因注释的共同语言。大规模的技术已经产生了许多有价值的蛋白质-蛋白质相互作用数据集，这些数据集对蛋白质功能的研究很有用。结合氧化石墨烯和蛋白质-蛋白质相互作用数据可以预测未知蛋白质的功能。结果：我们将马尔可夫随机场方法应用于基于多个蛋白-蛋白相互作用数据集的酵母蛋白功能预测。我们用表示该预测置信度的概率将功能分配给未知蛋白质。这些功能是基于氧化石墨烯中定义的三大类细胞成分、分子功能和生物过程。酵母蛋白在Saccharomyces Genome Database （SGD）中定义。蛋白质-蛋白质相互作用数据集来自慕尼黑蛋白质序列信息中心（MIPS），包括物理相互作用和遗传相互作用。我们的预测效率是通过将留一验证程序应用于功能路径匹配方案来衡量的，该方案将预测结果与GO对蛋白质功能的描述进行比较，从抽象水平到GO结构的详细水平。对于生物过程，留一验证方法的准确率和召回率均达到52%，明显优于单纯的关联罪责法。补充资料：http://www.cmb.usc.edu/similar tomsms/gomapping。
Motivation: Gene Ontology (GO) consortium provides structural description of protein function that is used as a common language for gene annotation in many organisms. Large-scale techniques have generated many valuable protein-protein interaction datasets that are useful for the study of protein function. Combining both GO and protein-protein interaction data allows the prediction of function for unknown proteins.Result: We apply a Markov random field method to the prediction of yeast protein function based on multiple protein-protein interaction datasets. We assign function to unknown proteins with a probability representing the confidence of this prediction. The functions are based on three general categories of cellular component, molecular function and biological process defined in GO. The yeast proteins are defined in the Saccharomyces Genome Database (SGD). The protein-protein interaction datasets are obtained from the Munich Information Center for Protein Sequences (MIPS), including physical interactions and genetic interactions. The efficiency of our prediction is measured by applying the leave-one-out validation procedure to a functional path matching scheme, which compares the prediction with the GO description of a protein's function from the abstract level to the detailed level along the GO structure. For biological process, the leave-one-out validation procedure shows 52% precision and recall of our method, much better than that of the simple guilty-by-association methods.Supplementary material: http://www.cmb.usc.edu/similar tomsms/gomapping.