Function Prediction for DNA-/RNA-Binding Proteins, GPCRs, and Drug ADME-Associated Proteins by SVM
Congzhong Cai, Han-Guang Xiao, Qianfei Yuan, Xinghua Liu, Yufeng Wen · Protein and Peptide Letters · 2008
This paper explores the use of support vector machine (SVM) for protein function prediction. Studies are conducted on several groups of proteins with different functions including DNA-binding proteins, RNA-binding proteins, Gprotein coupled receptors, drug absorption proteins, drug metabolizing enzymes, drug distribution and excretion proteins. The computed accuracy for the prediction of these proteins is found to be in the range of 82.32% to 99.7%, which illustrates the potential of SVM in facilitating protein function prediction. Keywords: Protein function prediction, DNA-binding proteins, RNA-binding proteins, G-protein coupled receptors (GPCRs), drug absorption proteins, drug metabolizing enzymes, drug distribution and excretion proteins, support vector machine (SVM)