DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.

ABSTRACT:

Motivation

Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, 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 candidate drugs or 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 is the most powerful technique in the feature-based methods. 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

SUBMITTER: Yuan Q 

PROVIDER: S-EPMC4908328 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature