GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined ...GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.展开更多
文摘现有的Web服务发现方法存在两方面的问题:一方面,采用语法级 Web服务描述语言,因语义信息不足和依赖关键字匹配,容易造成查准率低,影响服务复用和服务组合的相容性.另一方面,采用语义级 Web服务描述语言,因缺乏服务质量描述和灵活、有效的服务匹配算法,而难以保证服务组合的性能和质量.作者首先在系统研究Web服务描述语言的基础上,设计了一种基于服务质量的轻量级Web服务描述语言QWSDL(Qos based Web Serv ice Description Language) ,全方位描述Web服务的功能、行为约束以及服务质量.其次,提出“三层次,五类型”的匹配模型,引进相似函数来度量松弛匹配的服务相似程度.最后,对比实验证明QWSDL和松弛匹配是可行和有效的.
基金the National Natural Science Foundation of China (Grant Nos. 30170515, 30370388, 30370798, 30570424 and 30571034),the National High Tech Development Project of China (Grant Nos. 2003AA2Z2051 and 2002AA2Z2052),+3 种基金Heilongjiang Science & Technology Key Project (Grant No. GB03C602-4),Harbin (City) Science & Technology Key Project (Grant No. 2003AA3CS113),Natural Science Foundation of Heilongjiang (Grant No. F0177 ),Outstanding Overseas Scientist Foundation of Education Department of Heilongjiang Province (Grant No. 1055HG009)
文摘GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.
