Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens.
Quick Facts
What This Study Found
None of the models significantly outperformed random predictions for immunogenic peptides.
Key Numbers
How They Did This
The study systematically evaluated several publicly available models for predicting CD8+ T cell targets in the context of pathogens and cancers.
Why This Research Matters
Improving predictions of T cell targets is crucial for developing effective vaccines and cancer therapies. Understanding model limitations can guide future research and model development.
What This Study Doesn't Tell Us
The study primarily focused on existing models without developing new predictive tools, and results may not directly translate to clinical settings.
Trust & Context
- Original Title:
- Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens.
- Published In:
- Briefings in bioinformatics, 23(3) (2022)
- Authors:
- Buckley, Paul R, Lee, Chloe H, Ma, Ruichong, Woodhouse, Isaac, Woo, Jeongmin, Tsvetkov, Vasily O, Shcherbinin, Dmitrii S, Antanaviciute, Agne, Shughay, Mikhail, Rei, Margarida, Simmons, Alison, Koohy, Hashem
- Database ID:
- RPEP-06021
Evidence Hierarchy
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Cite This Study
https://rethinkpeptides.com/research/RPEP-06021APA
Buckley, Paul R; Lee, Chloe H; Ma, Ruichong; Woodhouse, Isaac; Woo, Jeongmin; Tsvetkov, Vasily O; Shcherbinin, Dmitrii S; Antanaviciute, Agne; Shughay, Mikhail; Rei, Margarida; Simmons, Alison; Koohy, Hashem. (2022). Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens.. Briefings in bioinformatics, 23(3). https://doi.org/10.1093/bib/bbac141
MLA
Buckley, Paul R, et al. "Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens.." Briefings in bioinformatics, 2022. https://doi.org/10.1093/bib/bbac141
RethinkPeptides
RethinkPeptides Research Database. "Evaluating performance of existing computational models in p..." RPEP-06021. Retrieved from https://rethinkpeptides.com/research/buckley-2022-evaluating-performance-of-existing
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Study data sourced from PubMed, a service of the U.S. National Library of Medicine, National Institutes of Health.
This study breakdown was produced by the RethinkPeptides research team. We analyze and report published research findings without making health recommendations. All interpretations are based solely on the published abstract and study data.