Using Grey Model GM(2,1) and Pseudo Amino Acid Composition to Predict Protein Subcellular Location
Identifying the subcellular localization of proteins is particularly helpful in the functional annotation of gene products. Based on the concept of pseudo amino acid composition, a novel representation of protein sequence, grey pseudo amino acid (grey-PseAA) was introduced. The advantage by incorporating the grey-PseAA into the pseudo amino acid composition is that it can catch the essence of the overall sequence pattern of a protein and hence more effectively reflect its sequence-order effects. It was demonstrated thru the jackknife cross validation test and independent dataset test that the overall success rates by the new approach were significantly improved. It is anticipated that the concept of grey-PseAA composition can be also used to predict many other protein attributes, such as membrane protein type, enzyme functional class, GPCR type, protease type, among many others.
Pseudo amino acid composition Protein subcellular location Grey model GM(2,1) Covariant-Discriminant algorithm grey-PseAA
Wei-Zhong Lin Xuan Xiao
School of Information Engineering Jing-De-Zhen Ceramic Institute Jing De Zheng, China Department of Machine and Electron Jing-De-Zhen Ceramic Institute Jing De Zhen, China
国际会议
上海
英文
718-721
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）