A novel approach to extracting features from motif content and protein composition for protein sequence classification.
This paper presents a novel approach to extracting features from motif content and protein composition for protein sequence classification. First, we formulate a protein sequence as a fixed-dimensional vector using the motif content and protein composition. Then, we further project the vectors into a low-dimensional space by the Principal Component Analysis (PCA) so that they can be represented by a combination of the eigenvectors of the covariance matrix of these vectors. Subsequently, the Genetic Algorithm (GA) is used to extract a subset of biological and functional sequence features from the eigen-space and to optimize the regularization parameter of the Support Vector Machine (SVM) simultaneously. Finally, we utilize the SVM classifiers to classify protein sequences into corresponding families based on the selected feature subsets. In comparison with the existing PSI-BLAST and SVM-pairwise methods, the experiments show the promising results of our approach.
Zhao
Xing-Ming
XM
Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui Province 230031, China.
Cheung
Yiu-Ming
YM
Huang
De-Shuang
DS
D020816:Amino Acid Motifs; D000818:Animals; D001185:Artificial Intelligence; D030562:Databases, Protein; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods* / Q000706:statistics & numerical data; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000706:statistics & numerical data; D011506:Proteins / Q000145:classification*; D012372:ROC Curve; D020539:Sequence Analysis, Protein; D013329:Structure-Activity Relationship
D011506:Proteins