title: Improving prediction accuracy of drug activities by utilising unlabelled instances with feature selection.
abstract: Molecular activities can be predicted by Quantitative Structure Activity Relationship (QSAR). Because of the high cost of experiments, the number of drug molecules with known activity is much less than that of unknown, to predict molecular activities utilising unlabelled instances will be an interesting issue. Here, Semi-Supervised Learning (SSL) is introduced and a SSL method, Co-Training is investigated on predicting drug activities utilising unlabelled instances. At the same time, a novel algorithm called FESCOT is proposed, which applies feature selection to remove redundant and irrelevant features for Co-Training. Numerical experimental results show that Co-Training and feature selection helps to improve the prediction ability of Co-Training.
authors.affiliation: School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China. gzli@shu.edu.cn
authors.forename: Guo-Zheng
authors.initials: GZ
authors.lastname: Li
authors.forename: Jack Y
authors.initials: JY
authors.lastname: Yang
authors.forename: Wen-Cong
authors.initials: WC
authors.lastname: Lu
authors.forename: Dan
authors.initials: D
authors.lastname: Li
authors.forename: Mary Qu
authors.initials: MQ
authors.lastname: Yang
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology; D003198:Computer Simulation; D016208:Databases, Factual; D015195:Drug Design; D008954:Models, Biological; D009152:Mutagenicity Tests / Q000706:statistics & numerical data; D021281:Quantitative Structure-Activity Relationship