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7篇 您的检索式：作者名="CHOU Kuochen"

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		题名	作者	年代	出处	被引量
	1	Ensemble classifier for protein fold pattern recognition 显示文摘	SHEN Hongbin CHOU Kuochen	2006	Bioinformatics2006，22，14：	1
	2	Prediction of G-protein-coupled receptor classes显示文摘	Chou Kuochen	2005	Journal of Proteome Research2005，4，4：	1
	3	Prediction of protein cellular attributes using pseudo-amino acid composition显示文摘	Chou Kuochen	2001	Proteins:Structure Function and Bioinformatics2001，43，3：	1
	4	MemType-2L:a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM显示文摘	Chou Kuochen Shen Hongbin	2007	Biochemical and Biophysical Research Communications2007，360，2：	1
	5	Molecular modeling of cytochrome P450 and drug metabolism显示文摘	Wang Jingfang Chou Kuochen	2010	Current Drug Metabolism2010，11，4：	1
	6	Boosting classifier for predicting protein domain structural class显示文摘	Kaiyan Feng Yudong Cai Kuochen Chou	2005	Biochemical and Biophysical Research Communications2005，334，1：	1
	7	PCA for predicting quaternary structure of protein显示文摘The number and arrangement of subunits that form a protein are referred to as quaternary structure.Knowing the quaternary structure of an uncharacterized protein provides clues to finding its biological function and interaction process with other molecules in a biological system.With the explosion of protein sequences generated in the Post-Genomic Age,it is vital to develop an automated method to deal with such a challenge.To explore this prob-lem,we adopted an approach based on the pseudo position-specific score matrix(Pse-PSSM)descriptor,proposed by Chou and Shen,representing a protein sample.The Pse-PSSM descriptor is advantageous in that it can combine the evolution information and sequence-correlated informa-tion.However,incorporating all these effects into a descriptor may cause‘high dimension disaster’.To over-come such a problem,the fusion approach was adopted by Chou and Shen.A completely different approach,linear dimensionality reduction algorithm principal component analysis(PCA)is introduced to extract key features from the high-dimensional Pse-PSSM space.The obtained dimension-reduced descriptor vector is a compact repre-sentation of the original high dimensional vector.The jack-knife test results indicate that the dimensionality reduction approach is efficient in coping with complicated problems in biological systems,such as predicting the quaternary struc-ture of proteins.	Tong WANG Hongbin SHEN Lixiu YAO Jie YANG Kuochen CHOU	2008	Frontiers of Electrical and Electronic Engineering in China2008，3，4：	0

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