Graph regularized non-negative matrix factorization with prior knowledge consistency constraint for drug-target interactions prediction.

ABSTRACT:

Background

Identifying drug-target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are expensive and time consuming. Effective computational methods to predict DTIs are useful to narrow the searching scope of potential drugs and speed up the process of drug discovery. There are a variety of non-negativity matrix factorization based methods to predict DTIs, but the convergence of the algorithms used in the matrix factorization are often overlooked and the results can be further improved.

Results

In order to predict DTIs more accurately and quickly, we propose an alternating direction algorithm to solve graph regularized non-negative matrix factorization with prior knowledge consistency constraint (ADA-GRMFC). Based on kno

SUBMITTER: Zhang J 

PROVIDER: S-EPMC9798666 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature