Protein Remote Homology Detection by Combining Chou's Pseudo Amino Acid Composition and Profile-Based Protein Representation

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	Protein Remote Homology Detection by Combining Chou's Pseudo Amino Acid Composition and Profile-Based Protein Representation
	Liu, Bin ; Wang, Xiaolong ; Zou, Quan ; Dong, Qiwen ; Chen, Qingcai ; Zou Q(邹权)
刊名	http://dx.doi.org/10.1002/minf.201300084
	2013
关键词	SUPPORT VECTOR MACHINES FUNCTIONAL DOMAIN COMPOSITION SUBCELLULAR LOCATION PREDICTION SEQUENTIAL EVOLUTION INFORMATION LATENT SEMANTIC ANALYSIS ENZYME SUBFAMILY CLASSES GO-PSEAA PREDICTOR ENSEMBLE CLASSIFIER WEB SERVER HYBRIDIZATION SPACE
英文摘要	National Natural Science Foundation of China [61173075, 61272383, 61001013]; Scientific Research Innovation Foundation in Harbin Institute of Technology [HIT.NS-RIF. 2010123, HIT.NSRIF. 2013103]; Natural Science Foundation of Guangdong Province [S2012040007390]; Scientific Research Foundation in Shenzhen [JC201005260159A, JC201005260175A]; Shenzhen International Cooperation Research Funding [GJHZ20120613110641217]; Shanghai Key Laboratory of Intelligent Information Processing, China [IIPL-2012-002]; Protein remote homology detection is a key problem in bioinformatics. Currently the discriminative methods, such as Support Vector Machine (SVM) can achieve the best performance. The most efficient approach to improve the performance of SVM-based methods is to find a general protein representation method that is able to convert proteins with different lengths into fixed length vectors and captures the different properties of the proteins for the discrimination. The bottleneck of designing the protein representation method is that native proteins have different lengths. Motivated by the success of the pseudo amino acid composition (PseAAC) proposed by Chou, we applied this approach for protein remote homology detection. Some new indices derived from the amino acid index (AAIndex) database are incorporated into the PseAAC to improve the generalization ability of this method. Finally, the performance is further improved by combining the modified PseAAC with profile-based protein representation containing the evolutionary information extracted from the frequency profiles. Our experiments on a well-known benchmark show this method achieves superior or comparable performance with current state-of-the-art methods.
语种	英语
出版者	WILEY-V C H VERLAG GMBH
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92529]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Liu, Bin,Wang, Xiaolong,Zou, Quan,et al. Protein Remote Homology Detection by Combining Chou's Pseudo Amino Acid Composition and Profile-Based Protein Representation[J]. http://dx.doi.org/10.1002/minf.201300084,2013.
APA	Liu, Bin,Wang, Xiaolong,Zou, Quan,Dong, Qiwen,Chen, Qingcai,&邹权.(2013).Protein Remote Homology Detection by Combining Chou's Pseudo Amino Acid Composition and Profile-Based Protein Representation.http://dx.doi.org/10.1002/minf.201300084.
MLA	Liu, Bin,et al."Protein Remote Homology Detection by Combining Chou's Pseudo Amino Acid Composition and Profile-Based Protein Representation".http://dx.doi.org/10.1002/minf.201300084 (2013).