MLFLHMDA: predicting human microbe-disease association based on multi-view latent feature learning.

ABSTRACT:

Introduction

A growing body of research indicates that microorganisms play a crucial role in human health. Imbalances in microbial communities are closely linked to human diseases, and identifying potential relationships between microbes and diseases can help elucidate the pathogenesis of diseases. However, traditional methods based on biological or clinical experiments are costly, so the use of computational models to predict potential microbe-disease associations is of great importance.

Methods

In this paper, we present a novel computational model called MLFLHMDA, which is based on a Multi-View Latent Feature Learning approach to predict Human potential Microbe-Disease Associations. Specifically, we compute Gaussian interaction profile kernel similarity between diseases an

SUBMITTER: Chen Z 

PROVIDER: S-EPMC10869561 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature