PTDA-SWGCL: Predicting tRNA-Disease Associations using Supplementarily Weighted Graph Contrastive Learning
Author:
Affiliation:
1. Xinjiang University,School of Software,Urumqi,China
2. Central South University,School of Computer Science and Engineering,Changsha,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10385250/10385251/10386000.pdf?arnumber=10386000
Reference30 articles.
1. Recent Studies of RNase P+
2. tRNA recognition, processing, and disease: Hypotheses around an unorthodox type of RNase P in human mitochondria
3. tRNA-derived small RNAs target transposable element transcripts
4. LDICDL: LncRNA-disease association identification based on Collaborative Deep Learning
5. MLRDFM: a multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations
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