QQ客服 官方微博 反馈留言

	Query-Adaptive Ranking with Support Vector Machines for Protein Homology Prediction
	Fu, Yan ; Pan, Rong ; Yang, Qiang ; Gao, Wen
	2011
关键词	Protein homology prediction information retrieval ranking function machine learning support vector machine
英文摘要	Protein homology prediction is a crucial step in template-based protein structure prediction. The functions that rank the proteins in a database according to their homologies to a query protein is the key to the success of protein structure prediction. In terms of information retrieval, such functions are called ranking functions, and are often constructed by machine learning approaches. Different from traditional machine learning problems, the feature vectors in the ranking-function learning problem are not identically and independently distributed, since they are calculated with regard to queries and may vary greatly in statistical characteristics from query to query. At present, few existing algorithms make use of the query-dependence to improve ranking performance. This paper proposes a query-adaptive ranking-function learning algorithm for protein homology prediction. Experiments with the support vector machine (SVM) used as the benchmark learner demonstrate that the proposed algorithm can significantly improve the ranking performance of SVMs in the protein homology prediction task.; Biochemical Research Methods; Computer Science, Information Systems; Mathematical & Computational Biology; EI; CPCI-S(ISTP); 0
语种	英语
DOI标识	10.1007/978-3-642-21260-4_31
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/406286]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Fu, Yan,Pan, Rong,Yang, Qiang,et al. Query-Adaptive Ranking with Support Vector Machines for Protein Homology Prediction. 2011-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。