DrugE-Rank Improving Drug-Target Interaction Prediction of New Candidate Drugs or Targets by Ensembl

• 作 者: Qingjun Yuan Junning Gao Dongliang Wu Shihua Zhang Hiroshi Mamitsuka Shanfeng Zhu
• 机 构: ShanghaiKeyLabofIntelligentInformationProcessingandSchoolofComputerScience,FudanUniversity,Shanghai2
• 出 处: 第七届全国生物信息学与系统生物学学术大会
• 发表日期: 2016年10期
• 关键词: Drug target interaction prediction Learning to Rank Ensemble Learning

　　Motivation: Identifying drug-target interaction is an important task in drug discovery.To reduce heavy time and financial cost in experimental identification of drug-target interaction,many computational approaches have been proposed.Although these approaches have used many different principles,their performance is far from satisfactory,especially in predicting drug-target interactions of new drugs or new targets.Methods: Approaches based on machine learning for this problem can be divided into two types: feature based and similarity-based methods.Learning to rank (LTR) is the known,most powerful technique in the feature-based methods,while similarity-based methods are well-accepted,due to their idea of connecting the chemical and genomic spaces,represented by drug and target similarities,respectively.We propose a new method,Drug,E-Rank,to improve the performance of the problem by nicely combining the advantages of the two different types of the methods.That is,DrugE-Rank uses LTR,for which multiple well-known similarity-based methods can be used as components of ensemble learning.