Graph regularized non-negative matrix factorization with [Formula: see text] norm regularization terms 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 costly and time consuming. Effective computational methods to predict DTIs are useful to speed up the process of drug discovery. A variety of non-negativity matrix factorization based methods are proposed to predict DTIs, but most of them overlooked the sparsity of feature matrices and the convergence of adopted matrix factorization algorithms, therefore their performances can be further improved.

Results

In order to predict DTIs more accurately, we propose a novel method iPALM-DLMF. iPALM-DLMF models DTIs prediction as a problem of non-negative matrix factorization with graph dual regularization terms and [Formula: see text] norm regu

SUBMITTER: Zhang J 

PROVIDER: S-EPMC10548602 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature