A Protein Classification Method Based on Latent Semantic Analysis Export

@proceedings{citeulike:5803019, 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}, author = {Yuan, Yongsheng and Lin, Lei and Dong, Qiwen and Wang, Xiaolong and Li, Minghui}, citeulike-article-id = {5803019}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/IEMBS.2005.1616306}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1616306}, day = {10}, doi = {10.1109/IEMBS.2005.1616306}, journal = {Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the}, keywords = {latent-semantic-analysis, protein-classification, svm}, month = {April}, pages = {7738--7741}, posted-at = {2009-09-18 19:28:53}, priority = {2}, title = {A Protein Classification Method Based on Latent Semantic Analysis}, url = {http://dx.doi.org/10.1109/IEMBS.2005.1616306}, year = {2006} }

TY - CONF ID - citeulike:5803019 L3 - citeulike-article-id:5803019 N2 - 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 JF - Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the EP - 7741 TI - A Protein Classification Method Based on Latent Semantic Analysis SP - 7738 KW - latent-semantic-analysis KW - protein-classification KW - svm AU - Yuan, Yongsheng AU - Lin, Lei AU - Dong, Qiwen AU - Wang, Xiaolong AU - Li, Minghui PY - 2006/04/10/ UR - http://dx.doi.org/10.1109/IEMBS.2005.1616306 ER -