Supervised Dimensionality Reduction Method for Predicting Membrane Proteins Types
With the rapid increase of protein sequences in the post-genomk age, the need for an automated and accurate tool to predict membrane protein types becomes increasingly important. Many efforts have been tried. Most of them aim to find the optimal classification scheme and less of them take the simplifying the complexity of biological system into consideration. This work shows how to decrease the complexity of biological system with the supervised DR (Dimensionality Reduction) method by transforming the original high-dimensional feature vectors into the low-dimensional feature vectors. Moreover, a powerful sequence encoding scheme by fusing PSSM (Position-Specific Score Matrix) and PseAA (Pseudo Amino Acid) method is used to represent the protein samples. Then, the K-NN (K-Nearest Neighbor) classifier is employed to identify the membrane protein types based on their reduced low-dimensional feature vectors. As a result, the jackknife and resubstitution test success rates on this model reach 85.2％ and 92.6％ resp ectively, and suggesting that the proposed approach is very promising for predicting membrane proteins types.
PSSM(Position-Specific Score Matrix) PseAA(Pseudo Amino Acid) Membrane protein LDAfLinear Discriminate Analysis)
Tong Wang Tian Xia Xiao-Ming Hu
Institute of Computer and Information,Shanghai Second Polytechnic University,Shanghai,201209,China
国际会议
南京
英文
451-454
2010-08-26（万方平台首次上网日期，不代表论文的发表时间）