QQ客服 官方微博 反馈留言

	Diffusion kernel-based logistic regression models for protein function prediction
	Lee, Hyunju ; Tu, Zhidong ; Deng, Minghua ; Sun, Fengzhu ; Chen, Ting
	2006
关键词	SACCHAROMYCES-CEREVISIAE EXPRESSION DATA YEAST GENOME SEQUENCE IDENTIFICATION LOCALIZATION PROGRAMS NETWORKS PATTERNS DATABASE
英文摘要	Assigning functions to unknown proteins is one of the most important problems in proteomics. Several approaches have used protein-protein interaction data to predict protein functions. We previously developed a Markov random field (MRF) based method to infer a protein's functions using protein-protein interaction data and the functional annotations of its protein interaction partners. In the original model, only direct interactions were considered and each function was considered separately. In this study, we develop a new model which extends direct interactions to all neighboring proteins, and one function to multiple functions. The goal is to understand a protein's function based on information on all the neighboring proteins in the interaction network. We first developed a novel kernel logistic regression (KLR) method based on diffusion kernels for protein interaction networks. The diffusion kernels provide means to incorporate all neighbors of proteins in the network. Second, we identified a set of functions that are highly correlated with the function of interest, referred to as the correlated functions, using the chi-square test. Third, the correlated functions were incorporated into our new KLR model. Fourth, we extended our model by incorporating multiple biological data sources such as protein domains, protein complexes, and gene expressions by converting them into networks. We showed that the KLR approach of incorporating all protein neighbors significantly improved the accuracy of protein function predictions over the MRF model. The incorporation of multiple data sets also improved prediction accuracy. The prediction accuracy is comparable to another protein function classifier based on the support vector machine (SVM), using a diffusion kernel. The advantages of the KLR model include its simplicity as well as its ability to explore the contribution of neighbors to the functions of proteins of interest.; Biotechnology & Applied Microbiology; Genetics & Heredity; SCI(E); 0; ARTICLE; 1; 40-55; 10
语种	英语
出处	SCI
出版者	omics a journal of integrative biology
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/398749]
专题	数学科学学院
推荐引用方式 GB/T 7714	Lee, Hyunju,Tu, Zhidong,Deng, Minghua,et al. Diffusion kernel-based logistic regression models for protein function prediction. 2006-01-01.

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。