Please use this identifier to cite or link to this item:
|Title:||Protein function classification via support vector machine approach||Authors:||Cai, C.Z.
Drug absorption protein
Drug distribution protein
Drug excretion protein
Drug metabolizing enzyme
Support vector machine
|Issue Date:||Oct-2003||Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 18, 2019
WEB OF SCIENCETM
checked on Oct 11, 2019
checked on Oct 13, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.