Supervised prediction of drug-target interactions using bipartite local models.

ABSTRACT:

Motivation

In silico prediction of drug-target interactions from heterogeneous biological data is critical in the search for drugs for known diseases. This problem is currently being attacked from many different points of view, a strong indication of its current importance. Precisely, being able to predict new drug-target interactions with both high precision and accuracy is the holy grail, a fundamental requirement for in silico methods to be useful in a biological setting. This, however, remains extremely challenging due to, amongst other things, the rarity of known drug-target interactions.

Results

We propose a novel supervised inference method to predict unknown drug-target interactions, represented as a bipartite graph. We use this method, known as bipartite local model

SUBMITTER: Bleakley K 

PROVIDER: S-EPMC2735674 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature