DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity.

ABSTRACT:

Motivation

Exploring potential associations between diseases can help in understanding pathological mechanisms of diseases and facilitating the discovery of candidate biomarkers and drug targets, thereby promoting disease diagnosis and treatment. Some computational methods have been proposed for measuring disease similarity. However, these methods describe diseases without considering their latent multi-molecule regulation and valuable supervision signal, resulting in limited biological interpretability and efficiency to capture association patterns.

Results

In this study, we propose a new computational method named DiSMVC. Different from existing predictors, DiSMVC designs a supervised graph collaborative framework to measure disease similarity. Multiple bio-entity associat

SUBMITTER: Wei H 

PROVIDER: S-EPMC11256965 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature