GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity is a research paper published in Briefings in Bioinformatics (2024). On theSindex it has a DataRank of 0. It has been cited 15 times.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
US National Science Foundation
Grant: IIS-2412195
US National Science Foundation
Grant: CCF-2400785
Cancer Prevention and Research Institute of Texas
Grant: RP230363
Cancer Prevention and Research Institute of Texas
Grant: RP230363/TW
National Institutes of Health
Grant: R01CA258584/TW
National Science Foundation
Grant: 2400785
EAGER: Integrating Multi-Omics Biological Networks and Ontologies for lncRNA Function Annotation using Deep Learning
National Science Foundation
Grant: 2412195
EAGER: Integrating Pathological Image and Biomedical Text Data for Clinical Outcome Prediction
National Institutes of Health
Grant: 5R01CA258584-04
Applying deep learning to predict T cell receptor binding specificity of neoantigens and response to checkpoint inhibitors
Fields of Study
MeSH Terms
Keywords