Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2105-6-174
Title: Protein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multiple support vector machines
Authors: Wang, J.
Sung, W.-K. 
Krishnan, A.
Li, K.-B.
Issue Date: 2005
Source: Wang, J., Sung, W.-K., Krishnan, A., Li, K.-B. (2005). Protein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multiple support vector machines. BMC Bioinformatics 6. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2105-6-174
Abstract: Background: Predicting the subcellular localization of proteins is important for determining the function of proteins. Previous works focused on predicting protein localization in Gram-negative bacteria obtained good results. However, these methods had relatively low accuracies for the localization of extracellular proteins. This paper studies ways to improve the accuracy for predicting extracellular localization in Gram-negative bacteria. Results: We have developed a system for predicting the subcellular localization of proteins for Gram-negative bacteria based on amino acid subalphabets and a combination of multiple support vector machines. The recall of the extracellular site and overall recall of our predictor reach 86.0% and 89.8%, respectively, in 5-fold cross-validation. To the best of our knowledge, these are the most accurate results for predicting subcellular localization in Gram-negative bacteria. Conclusion: Clustering 20 amino acids into a few groups by the proposed greedy algorithm provides a new way to extract features from protein sequences to cover more adjacent amino acids and hence reduce the dimensionality of the input vector of protein features. It was observed that a good amino acid grouping leads to an increase in prediction performance. Furthermore, a proper choice of a subset of complementary support vector machines constructed by different features of proteins maximizes the prediction accuracy. © 2005 Wang et al; licensee BioMed Central Ltd.
Source Title: BMC Bioinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/39078
ISSN: 14712105
DOI: 10.1186/1471-2105-6-174
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