Prediction of Enzyme Subfamily Class via Pseudo Amino Acid Composition by Incorporating the Conjoint Triad Feature

NWIPB OpenIR

	Prediction of Enzyme Subfamily Class via Pseudo Amino Acid Composition by Incorporating the Conjoint Triad Feature
	Wang, Yong-Cui 1,2; Wang, Xiao-Bo 1; Yang, Zhi-Xia 1,3; Deng, Nai-Yang 1
	2010-11-01
发表期刊	PROTEIN AND PEPTIDE LETTERS
ISSN	0929-8665
卷号	17 期号:11 页码:1441-1449
文章类型	Article
摘要	Predicting enzyme subfamily class is an imbalance multi-class classification problem due to the fact that the number of proteins in each subfamily makes a great difference. In this paper, we focus on developing the computational methods specially designed for the imbalance multi-class classification problem to predict enzyme subfamily class. We compare two support vector machine (SVM)-based methods for the imbalance problem, AdaBoost algorithm with RBFSVM (SVM with RBF kernel) and SVM with arithmetic mean (AM) offset (AM-SVM) in enzyme subfamily classification. As input features for our predictive model, we use the conjoint triad feature (CTF). We validate two methods on an enzyme benchmark dataset, which contains six enzyme main families with a total of thirty-four subfamily classes, and those proteins have less than 40% sequence identity to any other in a same functional class. In predicting oxidoreductases subfamilies, AM-SVM obtains the over 0.92 Matthew's correlation coefficient (MCC) and over 93% accuracy, and in predicting lyases, isomerases and ligases subfamilies, it obtains over 0.73 MCC and over 82% accuracy. The improvement in the predictive performance suggests the AM-SVM might play a complementary role to the existing function annotation methods.; Predicting enzyme subfamily class is an imbalance multi-class classification problem due to the fact that the number of proteins in each subfamily makes a great difference. In this paper, we focus on developing the computational methods specially designed for the imbalance multi-class classification problem to predict enzyme subfamily class. We compare two support vector machine (SVM)-based methods for the imbalance problem, AdaBoost algorithm with RBFSVM (SVM with RBF kernel) and SVM with arithmetic mean (AM) offset (AM-SVM) in enzyme subfamily classification. As input features for our predictive model, we use the conjoint triad feature (CTF). We validate two methods on an enzyme benchmark dataset, which contains six enzyme main families with a total of thirty-four subfamily classes, and those proteins have less than 40% sequence identity to any other in a same functional class. In predicting oxidoreductases subfamilies, AM-SVM obtains the over 0.92 Matthew's correlation coefficient (MCC) and over 93% accuracy, and in predicting lyases, isomerases and ligases subfamilies, it obtains over 0.73 MCC and over 82% accuracy. The improvement in the predictive performance suggests the AM-SVM might play a complementary role to the existing function annotation methods.
关键词	Enzyme Subfamily Class Prediction Conjoint Triad Feature Imbalance Problem Support Vector Machine
WOS标题词	Science & Technology ; Life Sciences & Biomedicine
关键词[WOS]	SUPPORT VECTOR MACHINES ; PROTEIN STRUCTURAL CLASSES ; SUBCELLULAR LOCATION PREDICTION ; FUNCTIONAL DOMAIN COMPOSITION ; COMPLEXITY MEASURE FACTOR ; APOPTOSIS PROTEINS ; CLEAVAGE SITES ; GRAPHIC RULES ; TURN TYPES ; KINETICS
收录类别	SCI
语种	英语
WOS研究方向	Biochemistry & Molecular Biology
WOS类目	Biochemistry & Molecular Biology
WOS记录号	WOS:000284651900017
引用统计	被引频次：53[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/363003/1640
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.Xinjiang Univ, Coll Math & Syst Sci, Urumuchi 830046, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yong-Cui,Wang, Xiao-Bo,Yang, Zhi-Xia,et al. Prediction of Enzyme Subfamily Class via Pseudo Amino Acid Composition by Incorporating the Conjoint Triad Feature[J]. PROTEIN AND PEPTIDE LETTERS,2010,17(11):1441-1449.
APA	Wang, Yong-Cui,Wang, Xiao-Bo,Yang, Zhi-Xia,&Deng, Nai-Yang.(2010).Prediction of Enzyme Subfamily Class via Pseudo Amino Acid Composition by Incorporating the Conjoint Triad Feature.PROTEIN AND PEPTIDE LETTERS,17(11),1441-1449.
MLA	Wang, Yong-Cui,et al."Prediction of Enzyme Subfamily Class via Pseudo Amino Acid Composition by Incorporating the Conjoint Triad Feature".PROTEIN AND PEPTIDE LETTERS 17.11(2010):1441-1449.