Centre for Bioinformatics, Imperial College London, London, SW7 2AZ, UK.
Nucleic Acids Res. 2012 Jul;40(Web Server issue):W466-70. doi: 10.1093/nar/gks489. Epub 2012 May 27.
Only a small fraction of known proteins have been functionally characterized, making protein function prediction essential to propose annotations for uncharacterized proteins. In recent years many function prediction methods have been developed using various sources of biological data from protein sequence and structure to gene expression data. Here we present the CombFunc web server, which makes Gene Ontology (GO)-based protein function predictions. CombFunc incorporates ConFunc, our existing function prediction method, with other approaches for function prediction that use protein sequence, gene expression and protein-protein interaction data. In benchmarking on a set of 1686 proteins CombFunc obtains precision and recall of 0.71 and 0.64 respectively for gene ontology molecular function terms. For biological process GO terms precision of 0.74 and recall of 0.41 is obtained. CombFunc is available at http://www.sbg.bio.ic.ac.uk/combfunc.
已知蛋白质中只有一小部分具有功能特征，因此蛋白质功能预测对于提出未鉴定蛋白质的注释至关重要。近年来，已经开发了许多使用各种生物数据来源（从蛋白质序列和结构到基因表达数据）的功能预测方法。在这里，我们展示了 CombFunc 网络服务器，它可以进行基于基因本体论 (GO) 的蛋白质功能预测。CombFunc 将我们现有的功能预测方法 ConFunc 与其他使用蛋白质序列、基因表达和蛋白质-蛋白质相互作用数据的功能预测方法相结合。在对 1686 个蛋白质的基准测试中，CombFunc 分别获得了基因本体论分子功能术语的 0.71 和 0.64 的精度和召回率。对于生物过程 GO 术语，获得了 0.74 的精度和 0.41 的召回率。CombFunc 可在 http://www.sbg.bio.ic.ac.uk/combfunc 获得。
