Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context

doi:10.1186/1752-0509-5-S1-S6

CSpace > 应用数学研究所

	Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context
	Wang, Yong-Cui 2,3; Wang, Yong4 ; Yang, Zhi-Xia 1; Deng, Nai-Yang 2
	2011-06-20
发表期刊	BMC SYSTEMS BIOLOGY
ISSN	1752-0509
卷号	5 页码:11
摘要	Background: Enzymes are known as the largest class of proteins and their functions are usually annotated by the Enzyme Commission (EC), which uses a hierarchy structure, i.e., four numbers separated by periods, to classify the function of enzymes. Automatically categorizing enzyme into the EC hierarchy is crucial to understand its specific molecular mechanism. Results: In this paper, we introduce two key improvements in predicting enzyme function within the machine learning framework. One is to introduce the efficient sequence encoding methods for representing given proteins. The second one is to develop a structure-based prediction method with low computational complexity. In particular, we propose to use the conjoint triad feature (CTF) to represent the given protein sequences by considering not only the composition of amino acids but also the neighbor relationships in the sequence. Then we develop a support vector machine (SVM)-based method, named as SVMHL (SVM for hierarchy labels), to output enzyme function by fully considering the hierarchical structure of EC. The experimental results show that our SVMHL with the CTF outperforms SVMHL with the amino acid composition (AAC) feature both in predictive accuracy and Matthew's correlation coefficient (MCC). In addition, SVMHL with the CTF obtains the accuracy and MCC ranging from 81% to 98% and 0.82 to 0.98 when predicting the first three EC digits on a low-homologous enzyme dataset. We further demonstrate that our method outperforms the methods which do not take account of hierarchical relationship among enzyme categories and alternative methods which incorporate prior knowledge about inter-class relationships. Conclusions: Our structure-based prediction model, SVMHL with the CTF, reduces the computational complexity and outperforms the alternative approaches in enzyme function prediction. Therefore our new method will be a useful tool for enzyme function prediction community.
DOI	10.1186/1752-0509-5-S1-S6
语种	英语
资助项目	National Natural Science Foundation of China[10801131] ; National Natural Science Foundation of China[10801112] ; National Natural Science Foundation of China[10971223] ; National Natural Science Foundation of China[11071252] ; Ph.D Graduate Start Research Foundation of Xinjiang University[BS080101] ; SRF for ROCS, SEM ; Shanghai Key Laboratory of Intelligent Information Processing[IIPL-2010-008]
WOS研究方向	Mathematical & Computational Biology
WOS类目	Mathematical & Computational Biology
WOS记录号	WOS:000297313700005
出版者	BIOMED CENTRAL LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/240
专题	应用数学研究所
通讯作者	Yang, Zhi-Xia
作者单位	1.Xinjiang Univ, Coll Math & Syst Sci, Urumuchi 830046, Peoples R China 2.China Agr Univ, Coll Sci, Beijing 100083, Peoples R China 3.Chinese Acad Sci, NW Inst Plateau Biol, Key Lab Adaptat & Evolut Plateau Biota, Xining 810001, Peoples R China 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yong-Cui,Wang, Yong,Yang, Zhi-Xia,et al. Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context[J]. BMC SYSTEMS BIOLOGY,2011,5:11.
APA	Wang, Yong-Cui,Wang, Yong,Yang, Zhi-Xia,&Deng, Nai-Yang.(2011).Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context.BMC SYSTEMS BIOLOGY,5,11.
MLA	Wang, Yong-Cui,et al."Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context".BMC SYSTEMS BIOLOGY 5(2011):11.