Weighted feature value based Drug Target Protein prediction.
Drug discovery is a long process in which only a few successful new therapeutic discoveries are made and identification of drug target candidate proteins requires considerable time and efforts. However, the accumulation of information on drugs has made it possible to devise new computational methods for classifying drug target candidates. In this paper, we devise a Drug Target Protein (DT-P) classification method by the summation of weighted features which is extracted from known DT-P. The method is validated using Bayesian decision theory and SVM, and it was revealed to achieve high specificity of 89.5% with 88% accuracy.
Hyun
Bo-ra
BR
School of Engineering, Information and Communication University, Yuseong-gu, Daejeon, Korea. lovesun79ya@icu.ac.kr
Jung
Hwiesung
H
Jang
Woo-Hyuk
WH
Jung
Suk Hoon
SH
Han
Dong-Soo
DS
D001499:Bayes Theorem; D016208:Databases, Factual; D003657:Decision Making; D004304:Dosage Forms; D004333:Drug Administration Routes; D004337:Drug Carriers; D016503:Drug Delivery Systems / Q000379:methods*; D004358:Drug Therapy / Q000379:methods; D006801:Humans; D004364:Pharmaceutical Preparations / Q000145:classification; D011506:Proteins / Q000187:drug effects / Q000378:metabolism; D015203:Reproducibility of Results
D004304:Dosage Forms; D004337:Drug Carriers; D004364:Pharmaceutical Preparations; D011506:Proteins