GCNMF-SDA: predicting snoRNA-disease associations based on graph convolution and non-negative matrix factorization.

ABSTRACT: Small nucleolar RNAs (snoRNAs) play crucial roles in a wide range of biological processes, and studying their association with diseases can enhance our understanding of disease pathogenesis. Nevertheless, current knowledge of these associations is limited traditional biological experiments are both costly and time-consuming. Consequently, developing efficient computational methods is essential for predicting potential snoRNA-disease associations. We propose a novel prediction method based on non-negative matrix factorization and graph convolution for predicting snoRNA-disease associations (GCNMF-SDA). First, five different types of similarity information from snoRNA and disease entities are introduced to fully mine and refine the feature information. Then the snoRNA and disease similarity

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12409419 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature