CDPMF-DDA: contrastive deep probabilistic matrix factorization for drug-disease association prediction

CDPMF-DDA: contrastive deep probabilistic matrix factorization for drug-disease association prediction

Abstract The process of new drug development is complex, whereas drug-disease association (DDA) prediction aims to identify new therapeutic uses for existing medications. However, existing graph contrastive learning approaches typically rely on single-view contrastive learning, which struggle to ful...

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Bibliographic Details
Main Authors: Xianfang Tang, Yawen Hou, Yajie Meng, Zhaojing Wang, Changcheng Lu, Juan Lv, Xinrong Hu, Junlin Xu, Jialiang Yang
Format: Article
Language:English
Published: BMC 2025-01-01
Series:BMC Bioinformatics
Subjects:
Drug-disease association prediction
Contrastive learning
Matrix factorization
Multiple contrastive views
Online Access:https://doi.org/10.1186/s12859-024-06032-w
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https://doi.org/10.1186/s12859-024-06032-w

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