Genome-scale gene function prediction using multiple sources of high-throughput data in yeast Saccharomyces cerevisiae
Genome-scale gene function prediction using multiple sources of high-throughput data in yeast Saccharomyces cerevisiae
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DOI：
10.1089/omi.2004.8.322
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发表时间：
2004-12-01
影响因子：
3.3
通讯作者：
Xu, D
中科院分区：
文献类型：
--
作者：
Joshi, T;Chen, Y;Xu, D
后基因组时代基因功能的研究是基因组学研究的重要课题之一。为了应对这一挑战，我们开发了GeneFAS（基因功能注释系统），一种新的综合概率方法，通过结合蛋白质-蛋白质相互作用，蛋白质复合物，微阵列基因表达谱和已知蛋白质的注释信息，通过综合统计模型进行细胞功能预测。我们的方法是基于一种新的评估之间的关系（1）的相互作用/两种蛋白质的高通量数据的相关性和（2）他们的功能关系，在他们的基因本体论（GO）层次。我们已经为预测开发了一个Web服务器。我们已经将我们的方法应用于酵母酿酒酵母和预测功能的2472未注释的蛋白质中的1548。
Characterizing gene function is one of the major challenging tasks in the post-genomic era. To address this challenge, we have developed GeneFAS (Gene Function Annotation System), a new integrated probabilistic method for cellular function prediction by combining information from protein-protein interactions, protein complexes, microarray gene expression profiles, and annotations of known proteins through an integrative statistical model. Our approach is based on a novel assessment for the relationship between (1) the interaction/correlation of two proteins' high-throughput data and (2) their functional relationship in terms of their Gene Ontology (GO) hierarchy. We have developed a Web server for the predictions. We have applied our method to yeast Saccharomyces cerevisiae and predicted functions for 1548 out of 2472 unannotated proteins.