Application of latent semantic analysis to protein remote homology detection.
BACKGROUND
Remote homology detection between protein sequences is a central problem in computational biology. The discriminative method such as the support vector machine (SVM) is one of the most effective methods. Many of the SVM-based methods focus on finding useful representations of protein sequence, using either explicit feature vector representations or kernel functions. Such representations may suffer from the peaking phenomenon in many machine-learning methods because the features are usually very large and noise data may be introduced. Based on these observations, this research focuses on feature extraction and efficient representation of protein vectors for SVM protein classification.


RESULTS
In this study, a latent semantic analysis (LSA) model, which is an efficient feature extraction technique from natural language processing, has been introduced in protein remote homology detection. Several basic building blocks of protein sequences have been investigated as the 'words' of 'protein sequence language', including N-grams, patterns and motifs. Each protein sequence is taken as a 'document' that is composed of bags-of-word. The word-document matrix is constructed first. The LSA is performed on the matrix to produce the latent semantic representation vectors of protein sequences, leading to noise-removal and smart description of protein sequences. The latent semantic representation vectors are then evaluated by SVM. The method is tested on the SCOP 1.53 database. The results show that the LSA model significantly improves the performance of remote homology detection in comparison with the basic formalisms. Furthermore, the performance of this method is comparable with that of the complex kernel methods such as SVM-LA and better than that of other sequence-based methods such as PSI-BLAST and SVM-pairwise.
Dong
Qi-Wen
QW
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. qwdong@insun.hit.edu.cn
Wang
Xiao-Long
XL
Lin
Lei
L
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D017124:Conserved Sequence; D008969:Molecular Sequence Data; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000032:analysis / Q000737:chemistry* / Q000145:classification; D012660:Semantics; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D012984:Software
D011506:Proteins