Interrogating noise in protein sequences from the perspective of protein-protein interactions prediction

NWIPB OpenIR

	Interrogating noise in protein sequences from the perspective of protein-protein interactions prediction
	Wang, Yongcui 2; Ren, Xianwen 3,4; Zhang, Chunhua 5; Deng, Naiyang 1; Zhang, Xiangsun 6
	2012-12-21
发表期刊	JOURNAL OF THEORETICAL BIOLOGY
ISSN	0022-5193
卷号	315 页码:64-70
文章类型	Article
摘要	The past decades witnessed extensive efforts to study the relationship among proteins. Particularly, sequence-based protein-protein interactions (PPIs) prediction is fundamentally important in speeding up the process of mapping interactomes of organisms. High-throughput experimental methodologies make many model organism's PPIs known, which allows us to apply machine learning methods to learn understandable rules from the available PPIs. Under the machine learning framework, the composition vectors are usually applied to encode proteins as real-value vectors. However, the composition vector value might be highly correlated to the distribution of amino acids, i.e., amino acids which are frequently observed in nature tend to have a large value of composition vectors. Thus formulation to estimate the noise induced by the background distribution of amino acids may be needed during representations. Here, we introduce two kinds of denoising composition vectors, which were successfully used in construction of phylogenetic trees, to eliminate the noise. When validating these two denoising composition vectors on Escherichia coli (E. coli), Saccharomyces cerevisiae (S. cerevisiae) and human PPIs datasets, surprisingly, the predictive performance is not improved, and even worse than non-denoised prediction. These results suggest that the noise in phylogenetic tree construction may be valuable information in PPIs prediction. (C) 2012 Elsevier Ltd. All rights reserved.; The past decades witnessed extensive efforts to study the relationship among proteins. Particularly, sequence-based protein-protein interactions (PPIs) prediction is fundamentally important in speeding up the process of mapping interactomes of organisms. High-throughput experimental methodologies make many model organism's PPIs known, which allows us to apply machine learning methods to learn understandable rules from the available PPIs. Under the machine learning framework, the composition vectors are usually applied to encode proteins as real-value vectors. However, the composition vector value might be highly correlated to the distribution of amino acids, i.e., amino acids which are frequently observed in nature tend to have a large value of composition vectors. Thus formulation to estimate the noise induced by the background distribution of amino acids may be needed during representations. Here, we introduce two kinds of denoising composition vectors, which were successfully used in construction of phylogenetic trees, to eliminate the noise. When validating these two denoising composition vectors on Escherichia coli (E. coli), Saccharomyces cerevisiae (S. cerevisiae) and human PPIs datasets, surprisingly, the predictive performance is not improved, and even worse than non-denoised prediction. These results suggest that the noise in phylogenetic tree construction may be valuable information in PPIs prediction. (C) 2012 Elsevier Ltd. All rights reserved.
关键词	Bioinformatics Denoising Composition Vector Machine Learning
WOS标题词	Science & Technology ; Life Sciences & Biomedicine
关键词[WOS]	AMINO-ACID-COMPOSITION ; SUBCELLULAR-LOCALIZATION ; INTERACTION NETWORKS ; INFORMATION ; COMPLEXES ; ALIGNMENT ; LOCATION
收录类别	SCI
语种	英语
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology
WOS类目	Biology ; Mathematical & Computational Biology
WOS记录号	WOS:000311194500007
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://210.75.249.4/handle/363003/3574
专题	中国科学院西北高原生物研究所
作者单位	1.China Agr Univ, Coll Sci, Beijing 100083, Peoples R China 2.Chinese Acad Sci, NW Inst Plateau Biol, Key Lab Adaptat & Evolut Plateau Biota, Xining 810001, Peoples R China 3.Chinese Acad Med Sci, Inst Pathogen Biol, MOH Key Lab Syst Biol Pathogens, Beijing 100730, Peoples R China 4.Peking Union Med Coll, Beijing 100730, Peoples R China 5.Renmin Univ China, Informat Sch, Beijing 100872, Peoples R China 6.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yongcui,Ren, Xianwen,Zhang, Chunhua,et al. Interrogating noise in protein sequences from the perspective of protein-protein interactions prediction[J]. JOURNAL OF THEORETICAL BIOLOGY,2012,315:64-70.
APA	Wang, Yongcui,Ren, Xianwen,Zhang, Chunhua,Deng, Naiyang,&Zhang, Xiangsun.(2012).Interrogating noise in protein sequences from the perspective of protein-protein interactions prediction.JOURNAL OF THEORETICAL BIOLOGY,315,64-70.
MLA	Wang, Yongcui,et al."Interrogating noise in protein sequences from the perspective of protein-protein interactions prediction".JOURNAL OF THEORETICAL BIOLOGY 315(2012):64-70.

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