ArticleiMetaOmics2024
Pep2TCR: Accurate prediction of CD4 T cell receptor binding specificity through transfer learning and ensemble approach.
Article in iMetaOmics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pep2TCR is an advanced deep learning model designed to predict cluster of differentiation 4 (CD4) T cell receptor (TCR) binding specificity, addressing the challenge posed by limited CD4 TCR data. It shows marked improvement over existing models. Pep2TCR is accessible via a user-friendly website for predicting CD4 TCR specificity at http://pep2tcr.liuxslab.com. This innovative tool holds promise for advancing personalized cancer immunotherapies.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.