DAE-CFR: detecting microRNA-disease associations using deep autoencoder and combined feature representation.

ABSTRACT:

Background

MicroRNA (miRNA) has been shown to play a key role in the occurrence and progression of diseases, making uncovering miRNA-disease associations vital for disease prevention and therapy. However, traditional laboratory methods for detecting these associations are slow, strenuous, expensive, and uncertain. Although numerous advanced algorithms have emerged, it is still a challenge to develop more effective methods to explore underlying miRNA-disease associations.

Results

In the study, we designed a novel approach on the basis of deep autoencoder and combined feature representation (DAE-CFR) to predict possible miRNA-disease associations. We began by creating integrated similarity matrices of miRNAs and diseases, performing a logistic function transformation, balancin

SUBMITTER: Liu Y 

PROVIDER: S-EPMC10981315 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature