title: DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins.
abstract: Application of computational methods in drug discovery has received increased attention in recent years as a way to accelerate drug target prediction. Based on 443 sequence-derived protein features, we applied the most commonly used machine learning methods to predict whether a protein is druggable as well as to opt for superior algorithm in this task. In addition, feature selection procedures were used to provide the best performance of each classifier according to the optimum number of features. When run on all features, Neural Network was the best classifier, with 89.98% accuracy, based on a k-fold cross-validation test. Among all the algorithms applied, the optimum number of most-relevant features was 130, according to the Support Vector Machine-Feature Selection (SVM-FS) algorithm. This study resulted in the discovery of new drug target which potentially can be employed in cell signaling pathways, gene expression, and signal transduction. The DrugMiner web tool was developed based on the findings of this study to provide researchers with the ability to predict druggable proteins. DrugMiner is freely available at www.DrugMiner.org.
authors.affiliation: Research Center for Pharmaceutical Nanotechnology (RCPN), Tabriz University of Medical Sciences, Tabriz, Iran.
authors.forename: Ali Akbar
authors.initials: AA
authors.lastname: Jamali
authors.affiliation: Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Reza
authors.initials: R
authors.lastname: Ferdousi
authors.affiliation: Information Technology Center, The University of Zanjan, Zanjan, Iran.
authors.forename: Saeed
authors.initials: S
authors.lastname: Razzaghi
authors.affiliation: School of Information Technology and Mathematical Sciences, Division of Information Technology, Engineering and the Environment, The University of South Australia, Adelaide, SA, Australia.
authors.forename: Jiuyong
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran. Electronic address: rsafdari@tums.ac.ir.
authors.forename: Reza
authors.initials: R
authors.lastname: Safdari
authors.affiliation: School of Information Technology and Mathematical Sciences, Division of Information Technology, Engineering and the Environment, The University of South Australia, Adelaide, SA, Australia; Department of Genetics & Evolution, School of Biological Sciences, The University of Adelaide, Adelaide, SA, Australia; School of Biological Sciences, Faculty of Science and Engineering, Flinders University, Adelaide, SA, Australia. Electronic address: Esmaeil.Ebrahimie@unisa.edu.au.
authors.forename: Esmaeil
authors.initials: E
authors.lastname: Ebrahimie
mesh_terms: D000465:Algorithms; D055808:Drug Discovery; D006801:Humans; D000069550:Machine Learning; D011506:Proteins / Q000627:therapeutic use*
chemical_list: D011506:Proteins