Title :
A Protein Classification Method Based on Latent Semantic Analysis*
Author :
Yuan, Yongsheng ; Lin, Lei ; Dong, Qiwen ; Wang, Xiaolong ; Li, Minghui
Abstract :
In this paper a new method that uses latent semantic analysis (LSA) to denote a protein sequence is proposed for researching the protein classification problem. A protein is vectorized according to its content of biological words: patterns and motifs, which are generated by utilizing TEIRESIAS algorithm and MEME/MAST system respectively. More precise description vectors of proteins are obtained through employing LSA. Those vectors are used to classify proteins combined with the support vector machine (SVM). Experiments of family-level protein classification on Structural Classification of Proteins database show that the performance of this method is better than that of the other state-of-the-arts methods
Keywords :
biology computing; molecular biophysics; molecular configurations; proteins; semantic networks; support vector machines; MEME/MAST system; TEIRESIAS algorithm; latent semantic analysis; motifs; patterns; protein classification method; protein sequence; support vector machine; Databases; Dynamic programming; Heuristic algorithms; Hidden Markov models; Iterative methods; Machine learning algorithms; Protein sequence; Support vector machine classification; Support vector machines; Surface-mount technology;
Conference_Titel :
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Conference_Location :
Shanghai
Print_ISBN :
0-7803-8741-4
DOI :
10.1109/IEMBS.2005.1616306
