Please use this identifier to cite or link to this item: https://doi.org/10.1093/bioinformatics/btg317
Title: Efficient remote homology detection using local structure
Authors: Hou, Y.
Hsu, W. 
Lee, M.L. 
Bystroff, C.
Issue Date: 2003
Source: Hou, Y., Hsu, W., Lee, M.L., Bystroff, C. (2003). Efficient remote homology detection using local structure. Bioinformatics 19 (17) : 2294-2301. ScholarBank@NUS Repository. https://doi.org/10.1093/bioinformatics/btg317
Abstract: Motivation: The function of an unknown biological sequence can often be accurately inferred if we are able to map this unknown sequence to its corresponding homologous family. At present, discriminative methods such as SVM-Fisher and SVM-pairwise, which combine support vector machine (SVM) and sequence similarity, are recognized as the most accurate methods, with SVM-pairwise being the most accurate. However, these methods typically encode sequence information into their feature vectors and ignore the structure information. They are also computationally inefficient. Based on these observations, we present an alternative method for SVM-based protein classification. Our proposed method, SVM-I-sites, utilizes structure similarity for remote homology detection. Result: We run experiments on the Structural Classification of Proteins 1.53 data set. The results show that SVM-I-sites is more efficient than SVM-pairwise. Further, we find that SVM-I-sites outperforms sequence-based methods such as PSI-BLAST, SAM, and SVM-Fisher while achieving a comparable performance with SVM-pairwise.
Source Title: Bioinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/39377
ISSN: 13674803
DOI: 10.1093/bioinformatics/btg317
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