Faster and more accurate global protein function assignment from protein interaction networks using the MFGO algorithm

doi:10.1016/j.febslet.2006.02.053

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Faster and more accurate global protein function assignment from protein interaction networks using the MFGO algorithm
	Sun, SW ; Zhao, Y ; Jiao, YS ; Yin, YF ; Cai, L ; Zhang, Y ; Lu, HC ; Chen, RS ; Bu, DB
刊名	FEBS LETTERS
	2006-03-20
卷号	580 期号:7 页码:1891-1896
关键词	protein interaction network function prediction global optimization
ISSN号	0014-5793
DOI	10.1016/j.febslet.2006.02.053
英文摘要	Motivation Predicting protein function accurately is an important issue in the post-genomic era. To achieve this goal, several approaches have been proposed deduce the function of unclassified proteins through sequence similarity, co-expression profiles, and other information. Among these methods, the global optimization method (GOM) is an interesting and powerful tool that assigns functions to unclassified proteins based on their positions in a physical interactions network [Vazquez, A., Flammini, A., Maritan, A. and Vespignani, A. (2003) Global protein function prediction from protein-protein interaction networks, Nat. Biotechnol., 21, 697-700]. To boost both the accuracy and speed of GOM, a new prediction method, MFGO (modified and faster global optimization) is presented in this paper, which employs local optimal repetition method to reduce calculation time, and takes account of topological structure information to achieve a more accurate prediction. Conclusion On four proteins interaction datasets, including Vazquez dataset, YP dataset, DIP-core dataset, and SPK dataset, MFGO was tested and compared with the popular MR (majority rule) and GOM methods. Experimental results confirm MFGO's improvement on both speed and accuracy. Especially, NFGO method has a distinctive advantage in accurately predicting functions for proteins with few neighbors. Moreover, the robustness of the approach was validated both in a dataset containing a high percentage of unknown proteins and a disturbed dataset through random insertion and deletion. The analysis shows that a moderate amount of misplaced interactions do not preclude a reliable function assignment. (c) 2006 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
WOS研究方向	Biochemistry & Molecular Biology ; Biophysics ; Cell Biology
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000236338300033
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/10432]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Bu, DB
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Biophys, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Sun, SW,Zhao, Y,Jiao, YS,et al. Faster and more accurate global protein function assignment from protein interaction networks using the MFGO algorithm[J]. FEBS LETTERS,2006,580(7):1891-1896.
APA	Sun, SW.,Zhao, Y.,Jiao, YS.,Yin, YF.,Cai, L.,...&Bu, DB.(2006).Faster and more accurate global protein function assignment from protein interaction networks using the MFGO algorithm.FEBS LETTERS,580(7),1891-1896.
MLA	Sun, SW,et al."Faster and more accurate global protein function assignment from protein interaction networks using the MFGO algorithm".FEBS LETTERS 580.7(2006):1891-1896.