Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0025-5564(03)00096-8
Title: Protein function classification via support vector machine approach
Authors: Cai, C.Z. 
Wang, W.L.
Sun, L.Z.
Chen, Y.Z. 
Keywords: Classification
Drug absorption protein
Drug distribution protein
Drug excretion protein
Drug metabolizing enzyme
Protein homodimer
RNA-binding protein
Support vector machine
Issue Date: Oct-2003
Source: Cai, C.Z., Wang, W.L., Sun, L.Z., Chen, Y.Z. (2003-10). Protein function classification via support vector machine approach. Mathematical Biosciences 185 (2) : 111-122. ScholarBank@NUS Repository. https://doi.org/10.1016/S0025-5564(03)00096-8
Abstract: Support vector machine (SVM) is introduced as a method for the classification of proteins into functionally distinguished classes. Studies are conducted on a number of protein classes including RNA-binding proteins; protein homodimers, proteins responsible for drug absorption, proteins involved in drug distribution and excretion, and drug metabolizing enzymes. Testing accuracy for the classification of these protein classes is found to be in the range of 84-96%. This suggests the usefulness of SVM in the classification of protein functional classes and its potential application in protein function prediction. © 2003 Elsevier Inc. All rights reserved.
Source Title: Mathematical Biosciences
URI: http://scholarbank.nus.edu.sg/handle/10635/53353
ISSN: 00255564
DOI: 10.1016/S0025-5564(03)00096-8
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