HGTMDA: A Hypergraph Learning Approach with Improved GCN-Transformer for miRNA–Disease Association Prediction
Accumulating scientific evidence highlights the pivotal role of miRNA–disease association research in elucidating disease pathogenesis and developing innovative diagnostics. Consequently, accurately identifying disease-associated miRNAs has emerged as a prominent research topic in bioinformatics. Ad...
主要な著者: | , , , , |
---|---|
フォーマット: | 論文 |
言語: | English |
出版事項: |
MDPI AG
2024-07-01
|
シリーズ: | Bioengineering |
主題: | |
オンライン・アクセス: | https://www.mdpi.com/2306-5354/11/7/680 |