MetaMDA: explainable prediction of microbe-drug association utilizing random walks on a microbe-metabolite-drug heterogeneous network.

ABSTRACT:

Motivation

Human-associated microbes play a critical role in physiological processes and disease development, including cancer. Predicting microbe-drug associations (MDAs) can aid drug discovery and personalized medicine. However, existing methods cannot predict MDAs involving microbes or drugs absent from labeled data, and they fail to model the underlying biological mechanisms between microbes and drugs. To address these limitations, we propose a novel computational framework, named MetaMDA, for predicting MDAs by performing random walks on a microbe-metabolite-drug heterogeneous network. MetaMDA first constructs a heterogeneous graph that integrates microbes, metabolites, and drugs, enabling the modeling of complex biological interactions. A random walk algorithm with tailored t

SUBMITTER: Wang Q 

PROVIDER: S-EPMC12714396 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature