Neighborhood-based inference and restricted Boltzmann machine for microbe and drug associations prediction.

ABSTRACT:

Background

Efficient identification of microbe-drug associations is critical for drug development and solving problem of antimicrobial resistance. Traditional wet-lab method requires a lot of money and labor in identifying potential microbe-drug associations. With development of machine learning and publication of large amounts of biological data, computational methods become feasible.

Methods

In this article, we proposed a computational model of neighborhood-based inference (NI) and restricted Boltzmann machine (RBM) to predict potential microbe-drug association (NIRBMMDA) by using integrated microbe similarity, integrated drug similarity and known microbe-drug associations. First, NI was used to obtain a score matrix of potential microbe-drug associations by using differen

SUBMITTER: Cheng X 

PROVIDER: S-EPMC9387521 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature