Function prediction for DNA-/RNA-binding proteins, GPCRs, and drug ADME-associated proteins by SVM.
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, G-protein 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.
Cai
Congzhong
C
Department of Applied Physics, Chongqing University, Chongqing 400044, People's Republic of China. caiczh@gmail.com
Xiao
Hanguang
H
Yuan
Qianfei
Q
Liu
Xinghua
X
Wen
Yufeng
Y
D019295:Computational Biology / Q000379:methods*; D004268:DNA-Binding Proteins / Q000737:chemistry / Q000502:physiology*; D030562:Databases, Protein; D011506:Proteins / Q000737:chemistry / Q000502:physiology*; D016601:RNA-Binding Proteins / Q000737:chemistry / Q000502:physiology*; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry / Q000502:physiology*; D012680:Sensitivity and Specificity
D004268:DNA-Binding Proteins; D011506:Proteins; D016601:RNA-Binding Proteins; D043562:Receptors, G-Protein-Coupled