Pith. sign in

REVIEW 4 major objections 5 minor 43 references

DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read DapPep, a sequence-only model, predicts TCR-antigen binding for unseen peptides with ROC-AUC 0.816, outperforming prior tools.

desk verdict A plausible but unverified zero-shot TCR-peptide predictor: the architecture is new, but undocumented control construction, missing leak checks, and inconsistent numbers leave the headline claim unproven. read the letter →

arxiv 2411.17798 v1 pith:OUJTOZRI submitted 2024-11-26 q-bio.QM cs.AIcs.LG

Jiangbin Zheng , Qianhui Xu , Ruichen Xia , Stan Z. Li This is my paper · ORCID
classification q-bio.QMcs.AIcs.LG
keywords T-cellreceptorpeptidebindingpredictionzero-shotlearningdomainadaptationself-supervisedpre-trainingattentionmechanismneoantigenimmunoinformatics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces DapPep, a sequence-only model that predicts whether a T-cell receptor (TCR) binds a given antigenic peptide, with a focus on peptides never seen in training. The authors claim DapPep consistently outperforms existing tools across majority, few-shot, and zero-shot settings, and that its advantage is largest for unseen peptides: ROC-AUC 0.816 and PR-AUC 0.836 on a zero-shot benchmark, versus 0.744/0.755 for the prior state of the art PanPep. DapPep also scores best on a clinical task of sorting reactive T cells for gastrointestinal neoantigens (0.835/0.834). If these results hold, the model gives clinicians and vaccine designers a fast, general predictor for novel antigens without per-peptide fine-tuning.

What carries the argument

The load-bearing component is the TCR-peptide cross-attention module (T PRepr). It receives TCR features from the ESM-2-initialized TCR encoder as key and value, peptide features as query, and produces a joint representation that a linear decoder maps to a binding probability. Before supervised training, the same module is pre-trained in an inner-loop, peptide-reconstruction objective using an asymmetric auto-encoder (asyAE) that treats peptides as both input and output; the authors argue this self-supervised step, rather than direct transfer learning, is what lets the model generalize to peptides with few or no known binding TCRs.

What would settle it

One could train DapPep on the exact same data but with all ZeroSet peptides artificially added to the training set; if performance on the zero-shot benchmark jumps dramatically, the claimed generalization is mostly memorization. Similarly, if re-analysis shows any zero-shot test peptide overlapping the pre-training peptide set, or if experimentally validated non-binders among the control TCRs are shown to be rare, the reported AUC/PR-AUC advantage would need to be re-evaluated.

Watch

Extended reading notes

Core claim

The central claim is that a lightweight, peptide-agnostic architecture can learn TCR-peptide binding patterns that transfer to completely unseen peptides. DapPep encodes TCRs with an ESM-2-initialized transformer, encodes peptides with shallow self-attention, and combines them in a cross-attention module; before supervised binding training, that module is pre-trained as an asymmetric autoencoder to reconstruct peptide sequences, which the authors argue teaches it the essential characteristics of peptides. After transfer to the binding task with only an MSE regression loss, the model outperforms PanPep, pMTnet, ERGO2, and DLpTCR on the zero-shot benchmark and on a gastrointestinal neoantigen T-cell sorting task, with no fine-tuning on the target peptides.

Load-bearing premise

The reported generalization rests on the assumption that ZeroSet and FewSet peptides never appear in DapPep's training data and that the 60,333,379 control TCRs are true non-binders, not merely unobserved or unlabeled pairs.

Editorial extensions

If this is right

  • DapPep can rank candidate neoantigens for T-cell reactivity without requiring known peptide-specific TCR data, potentially accelerating neoantigen vaccine and adoptive cell therapy pipelines.
  • The zero-shot and few-shot gains hold without task-specific fine-tuning, so the model can be deployed directly on new peptides in high-throughput screening.
  • Because the model is sequence-only and lightweight, it can be applied at the scale of full TCR repertoire screens to filter reactive T cells before experimental validation.
  • The same two-stage recipe—self-supervised peptide reconstruction followed by supervised affinity regression—could be transferred to other receptor-ligand binding problems with sparse training data.
  • Comparisons on the gastrointestinal cancer dataset suggest the model can sort tumor-infiltrating lymphocytes by neoantigen specificity, a step toward personalized immunotherapy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial: the paper's claimed advantage would be most convincing if the authors showed that the 60-million-TCR control set contains true non-binders rather than unobserved pairs; if many controls are simply unlabeled, the absolute AUC numbers may be inflated even if relative rankings hold.
  • Editorial: the zero-shot result implies that peptide identity is not memorized but rather reconstructed from composition and motif patterns; a direct test would be to mutate a known antigen at anchor positions and check whether predicted affinity drops as expected.
  • Editorial: because the pre-training uses only peptide sequences from the training pairs, the method's generalization could depend on the diversity of peptides in the training set; extending the asyAE to an external peptide corpus might further improve zero-shot transfer.
  • Editorial: the architecture could be adapted to paired alpha/beta TCR sequences, which are increasingly available, and might improve accuracy for peptides where the beta chain alone is insufficient.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes DapPep, a TCR-peptide binding affinity prediction framework that combines an ESM-2-initialized TCR encoder, a shallow peptide encoder, a cross-attention module, and an asymmetric autoencoder (asyAE) pre-training objective. The method is evaluated on PanPep's majority, few-shot, and zero-shot benchmarks, where it is claimed to outperform PanPep and other baselines, and on a gastrointestinal neoantigen T-cell sorting task. The central claim is that DapPep generalizes to unseen peptides and data-scarce settings better than existing tools, with ROC-AUC 0.816 and PR-AUC 0.836 on unseen peptides and 0.835/0.834 on the clinical sorting task.

Significance. If the reported results are correct, DapPep would be a practical sequence-only predictor for novel antigens, with direct relevance to neoantigen therapy and vaccine design. The architecture is lightweight and the two-stage training idea (pre-training a peptide reconstruction module before supervised binding learning) is interesting. However, the evidence as presented does not yet establish the claim: there is no code or data release, no error bars or significance tests, no documentation of negative-control construction or peptide-disjoint splits, and there are inconsistencies between the text and Table I. The central message is plausible but not rigorously supported.

major comments (4)
  1. [Section III-A] The negative-control construction is underspecified and load-bearing. The text says only 'All the binding TCRs are balanced by a controlTCRset, where the controlTCRset contains 60,333,379 non-binding TCRs (negative samples).' It is not stated whether these 60M TCRs are experimentally confirmed non-binders or are unlabeled repertoire TCRs never tested against the relevant peptides. If they are unlabeled, the model can separate curated binders from background repertoire using distributional cues such as CDR3 length, V/J usage, or source cohort, rather than peptide-specific binding. This would inflate both ROC-AUC and PR-AUC and directly affect the zero-shot and few-shot claims. Please describe how the control set was constructed, whether any filtering or matching was applied, and provide overlap statistics between the positive and negative sets. Without this, the reported margins over PanPep are not interpretable.
  2. [Section III-B and Table I] The reported numbers are internally inconsistent. In Section III-B, DapPep's zero-shot results are ROC-AUC 0.787 and PR-AUC 0.815, while Table I (Unseen Peptides) reports 0.816 and 0.836. Baseline numbers also disagree: pMTnet is listed as 0.564/0.555 in Table I but 0.563/0.555 in the text; ERGO2 is 0.504/0.524 in Table I but 0.496/0.542 in the text; DLpTCR is 0.483/0.481 in Table I but 0.517/0.488 in the text. Because the central claim is the margin over PanPep, these discrepancies must be reconciled. The manuscript should present one consistent set of results and explain the source of the differences (e.g., different evaluation subsets or random seeds).
  3. [Section III-C and Section II-A] There is no evidence that the zero-shot and few-shot test peptides are truly unseen. The claim that 'the peptides in this dataset are not available in the training set of DapPep and other baseline tools' is not backed by any documentation of the split construction or peptide-level overlap statistics. In addition, the asyAE pre-training in Section II-A uses 'only the peptide sequences from the training set of TCR-peptide pairs,' so it is also necessary to confirm that ZeroSet/FewSet peptides are excluded from the pre-training corpus. Please provide peptide-overlap analysis between the training set, the pre-training set, and the ZeroSet/FewSet, and describe the exact provenance of the splits.
  4. [Section III-B and III-D] All results are single-point estimates with no variance, confidence intervals, or statistical significance tests. Given that the headline improvements over PanPep are roughly 9-11% in ROC-AUC, it is essential to know whether the difference is stable across random seeds or data subsamples. For the clinical T-cell sorting task, the PR-AUC difference between PanPep (0.781) and DapPep (0.834) is smaller relative to the apparent variability in the other comparisons. Please report at least three independent runs (or bootstrapped CIs) and, ideally, a paired significance test such as a McNemar or bootstrap test on the test pairs.
minor comments (5)
  1. [Equation (4)] Equation (4) contains an extra closing parenthesis: 'LDapPep = MSE(Scorepred, Scoretarget)).' should be 'LDapPep = MSE(Scorepred, Scoretarget).'
  2. [Figure 2 caption] The caption of Figure 2 includes 'Ref. Panpep(Fig. 2d)' followed by an unexplained code snippet in Chinese-style brackets; this appears to be a leftover artifact and should be removed.
  3. [Section III-A] The manuscript says the dataset follows PanPep but gives no dataset statistics (e.g., number of positive TCR-peptide pairs per split, number of peptides, TCR chain type, length distributions). A data-availability and reproducibility statement would be helpful.
  4. [Section IV] The conclusions list 'neonatal antigen' where 'neoantigen' is intended; the same typo appears in the abstract. Also, 'zero-setting' in Section III-B should be 'zero-shot setting.'
  5. [Section II-A Pre-training Objective] The pre-training objective cites a long list of references ([7],[8],[10],[11],[13],[24],[28]-[30],[35]-[43]) that appear largely unrelated to the asyAE framework; these citations do not support the specific claim being made and should be replaced with relevant prior work on autoencoders or sequence reconstruction.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: DapPep's central claims rest on external benchmarks and distinct training objectives, not on definitions or self-citations.

full rationale

The paper's central claim is an empirical comparison against external baselines (PanPep, pMTnet, ERGO2, DLpTCR) on datasets adopted from PanPep and on an independent gastrointestinal neoantigen dataset. The two training objectives are distinct: the asyAE pre-training reconstructs peptide sequences (Eqs. 1-2), while the binding affinity pipeline optimizes an MSE loss against experimentally derived binding targets (Eq. 4). No fitted parameter is renamed as a prediction, and no equation makes the reported ROC-AUC or PR-AUC values equal to training inputs by construction. The self-citation block around the asyAE framework is background citation for an autoencoder concept, not a load-bearing uniqueness theorem or an ansatz smuggled in via citation. Concerns about the construction of the 60,333,379 control TCRs or possible test-peptide leakage are data-quality and validation risks, not circularity; the paper asserts that ZeroSet peptides are absent from training, but even if that assertion is unsupported, it is a correctness risk rather than a circular derivation. The limitations section explicitly calls for external experimental validation, which further indicates that the authors do not treat their benchmark results as self-justifying. Overall, the derivation chain is self-contained with respect to circularity, and the appropriate finding is no significant circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

DapPep introduces no new biological entities, forces, or conserved quantities; the asymmetric autoencoder is an architecture, not a hypothesized entity. The load-bearing assumptions are about data splits, negative sampling, and transferability of ESM-2.

free parameters (4)
  • Network weights of DapPep = not reported
    All parameters in TRepr, PRepr, TPRepr, and BindDecoder are learned from training data. This is standard, but the central performance claim depends on these exact weights, which are not released.
  • Amino acid Word2Vec embeddings (Residue2Vec) = not reported
    Used in Equation 1 and throughout; the source corpus and training procedure for these embeddings are not specified, and they affect all representations.
  • Architecture hyperparameters = not reported
    Number of attention heads, layers, hidden dimensions, learning rate, batch size, and epochs are not given; these choices materially affect the reported scores.
  • Binding affinity target normalization = not reported
    Raw affinity or probability targets are mapped to [0,1] without specifying the normalization, which affects the MSE loss and all reported metrics.
assumptions (5)
  • domain assumption Test peptides in ZeroSet are not present in the training set of DapPep or any baseline.
    The zero-shot claim depends entirely on this; the paper only says 'Following PanPep' without showing split construction or contamination checks.
  • domain assumption ControlTCRset entries are true non-binding TCRs, not unobserved or unlabeled pairs.
    Negative samples are central to ROC-AUC and PR-AUC; if controls are generated or derived from unpaired data, the metrics may be inflated.
  • domain assumption ESM-2 representations transfer well to TCR sequences.
    TRepr is initialized with ESM-2; if TCRs lie outside ESM-2's domain, the representation quality is not guaranteed.
  • domain assumption Peptide reconstruction pre-training (asyAE) captures sequence semantics useful for binding.
    This is the method's core inductive bias; no analysis shows that reconstruction quality correlates with binding prediction success.
  • standard math Standard backpropagation, attention, and cross-entropy/MSE losses behave as expected.
    No formal guarantees are given; the standard deep learning toolkit is assumed without explicit verification.

how reviews work

0 comments
Cite this review

Pith. "Pith review of DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction." pith.science (2026). https://pith.science/paper/OUJTOZRI

@misc{pith2026241117798,
  author       = {Pith},
  title        = {Pith review of: DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OUJTOZRI}},
  note         = {Machine review of arXiv:2411.17798}
}
read the original abstract

Identifying T-cell receptors (TCRs) that interact with antigenic peptides provides the technical basis for developing vaccines and immunotherapies. The emergent deep learning methods excel at learning antigen binding patterns from known TCRs but struggle with novel or sparsely represented antigens. However, binding specificity for unseen antigens or exogenous peptides is critical. We introduce a domain-adaptive peptide-agnostic learning framework DapPep for universal TCR-antigen binding affinity prediction to address this challenge. The lightweight self-attention architecture combines a pre-trained protein language model with an inner-loop self-supervised regime to enable robust TCR-peptide representations. Extensive experiments on various benchmarks demonstrate that DapPep consistently outperforms existing tools, showcasing robust generalization capability, especially for data-scarce settings and unseen peptides. Moreover, DapPep proves effective in challenging clinical tasks such as sorting reactive T cells in tumor neoantigen therapy and identifying key positions in 3D structures.

Figures

Figures reproduced from arXiv: 2411.17798 by the authors.

Figure 1
Figure 1. Proposed DapPep for TCR-peptide binding affinity prediction. The training pipeline is divided into two stages: Stage 1 involves initializing the TCR [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Comparison to SOTA model (PanPep) of ROC-AUC and PR-AUC performances in the different settings and datasets. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

43 extracted references · 41 canonical work pages

  1. [1]

    Phenotypic analysis of antigen-specific t lymphocytes

    John D Altman, Paul AH Moss, Philip JR Goulder, Dan H Barouch, Michael G McHeyzer-Williams, John I Bell, Andrew J McMichael, and Mark M Davis. Phenotypic analysis of antigen-specific t lymphocytes. Science, 274(5284):94–96, 1996

    work page 1996

  2. [2]

    Quantifiable predictive features define epitope-specific t cell receptor repertoires

    Pradyot Dash, Andrew J Fiore-Gartland, Tomer Hertz, George C Wang, Shalini Sharma, Aisha Souquette, Jeremy Chase Crawford, E Bridie Clemens, Thi HO Nguyen, Katherine Kedzierska, et al. Quantifiable predictive features define epitope-specific t cell receptor repertoires. Nature, 547(7661):89–93, 2017

    work page 2017

  3. [3]

    Autoencoder based local t cell repertoire density can be used to classify samples and t cell receptors

    Shirit Dvorkin, Reut Levi, and Yoram Louzoun. Autoencoder based local t cell repertoire density can be used to classify samples and t cell receptors. PLoS Computational Biology , 17(7):e1009225, 2021

    work page 2021

  4. [4]

    Pan- peptide meta learning for t-cell receptor–antigen binding recognition

    Yicheng Gao, Yuli Gao, Yuxiao Fan, Chengyu Zhu, Zhiting Wei, Chi Zhou, Guohui Chuai, Qinchang Chen, He Zhang, and Qi Liu. Pan- peptide meta learning for t-cell receptor–antigen binding recognition. Nature Machine Intelligence , pages 1–14, 2023

    work page 2023

  5. [5]

    Detection of enriched t cell epitope specificity in full t cell receptor sequence repertoires

    Sofie Gielis, Pieter Moris, Wout Bittremieux, Nicolas De Neuter, Benson Ogunjimi, Kris Laukens, and Pieter Meysman. Detection of enriched t cell epitope specificity in full t cell receptor sequence repertoires. Frontiers in immunology, 10:2820, 2019

    work page 2019

  6. [6]

    Identifying specificity groups in the t cell receptor repertoire

    Jacob Glanville, Huang Huang, Allison Nau, Olivia Hatton, Lisa E Wagar, Florian Rubelt, Xuhuai Ji, Arnold Han, Sheri M Krams, Christina Pettus, et al. Identifying specificity groups in the t cell receptor repertoire. Nature, 547(7661):94–98, 2017

    work page 2017

  7. [7]

    Learning complete protein representation by deep coupling of sequence and structure

    Bozhen Hu, Cheng Tan, Jun Xia, Jiangbin Zheng, Yufei Huang, Lirong Wu, Yue Liu, Yongjie Xu, and Stan Z Li. Learning complete protein representation by deep coupling of sequence and structure. bioRxiv, pages 2023–07, 2023

    work page 2023

  8. [8]

    Protein language models and structure prediction: Connection and progression

    Bozhen Hu, Jun Xia, Jiangbin Zheng, Cheng Tan, Yufei Huang, Yongjie Xu, and Stan Z Li. Protein language models and structure prediction: Connection and progression. arXiv preprint arXiv:2211.16742 , 2022

    arXiv 2022

Show all 43 references
  1. [9]

    Analyzing the mycobacterium tuberculosis immune response by t-cell receptor clustering with gliph2 and genome-wide antigen screening

    Huang Huang, Chunlin Wang, Florian Rubelt, Thomas J Scriba, and Mark M Davis. Analyzing the mycobacterium tuberculosis immune response by t-cell receptor clustering with gliph2 and genome-wide antigen screening. Nature biotechnology, 38(10):1194–1202, 2020

    2020

  2. [10]

    Protein 3d graph structure learning for robust structure-based protein property prediction

    Yufei Huang, Siyuan Li, Lirong Wu, Jin Su, Haitao Lin, Odin Zhang, Zihan Liu, Zhangyang Gao, Jiangbin Zheng, and Stan Z Li. Protein 3d graph structure learning for robust structure-based protein property prediction. In Proceedings of the AAAI Conference on Artificial Intellige...

    2024

  3. [11]

    Data-efficient protein 3d geometric pretraining via refinement of diffused protein structure decoy

    Yufei Huang, Lirong Wu, Haitao Lin, Jiangbin Zheng, Ge Wang, and Stan Z Li. Data-efficient protein 3d geometric pretraining via refinement of diffused protein structure decoy. arXiv preprint arXiv:2302.10888 , 2023

    2023 arXiv

  4. [12]

    Predicting recognition between t cell receptors and epitopes with tcrgp

    Emmi Jokinen, Jani Huuhtanen, Satu Mustjoki, Markus Heinonen, and Harri L ¨ahdesm¨aki. Predicting recognition between t cell receptors and epitopes with tcrgp. PLoS computational biology, 17(3):e1008814, 2021

    2021

  5. [13]

    Technical approaches to chinese sign language processing: a review

    Suhail Muhammad Kamal, Yidong Chen, Shaozi Li, Xiaodong Shi, and Jiangbin Zheng. Technical approaches to chinese sign language processing: a review. IEEE Access, 7:96926–96935, 2019

    2019

  6. [14]

    Sinks, suppressors and antigen presen- ters: how lymphodepletion enhances t cell-mediated tumor immunother- apy

    Christopher A Klebanoff, Hung T Khong, Paul A Antony, Douglas C Palmer, and Nicholas P Restifo. Sinks, suppressors and antigen presen- ters: how lymphodepletion enhances t cell-mediated tumor immunother- apy. Trends in immunology, 26(2):111–117, 2005

    2005

  7. [15]

    T-scan: a genome-wide method for the systematic discovery of t cell epitopes

    Tomasz Kula, Mohammad H Dezfulian, Charlotte I Wang, Nouran S Abdelfattah, Zachary C Hartman, Kai W Wucherpfennig, Herbert Kim Lyerly, and Stephen J Elledge. T-scan: a genome-wide method for the systematic discovery of t cell epitopes. Cell, 178(4):1016–1028, 2019

    2019

  8. [16]

    Language models of protein sequences at the scale of evolution enable accurate structure prediction

    Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, et al. Language models of protein sequences at the scale of evolution enable accurate structure prediction. bioRxiv, 2022

    2022

  9. [17]

    Neoantigen vaccines pass the immunogenicity test

    Gerald P Linette and Beatriz M Carreno. Neoantigen vaccines pass the immunogenicity test. Trends in molecular medicine , 23(10):869–871, 2017

    2017

  10. [18]

    Deep learning-based prediction of the t cell receptor–antigen binding specificity

    Tianshi Lu, Ze Zhang, James Zhu, Yunguan Wang, Peixin Jiang, Xue Xiao, Chantale Bernatchez, John V Heymach, Don L Gibbons, Jun Wang, et al. Deep learning-based prediction of the t cell receptor–antigen binding specificity. Nature machine intelligence , 3(10):864–875, 2021

    2021

  11. [19]

    Nettcr- 2.0 enables accurate prediction of tcr-peptide binding by using paired tcrα and β sequence data

    Alessandro Montemurro, Viktoria Schuster, Helle Rus Povlsen, Amalie Kai Bentzen, Vanessa Jurtz, William D Chronister, Austin Crinklaw, Sine R Hadrup, Ole Winther, Bjoern Peters, et al. Nettcr- 2.0 enables accurate prediction of tcr-peptide binding by using paired tcrα and β se...

    2021

  12. [20]

    An immunogenic personal neoantigen vaccine for patients with melanoma

    Patrick A Ott, Zhuting Hu, Derin B Keskin, Sachet A Shukla, Jing Sun, David J Bozym, Wandi Zhang, Adrienne Luoma, Anita Giobbie-Hurder, Lauren Peter, et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature, 547(7662):217–221, 2017

    2017

  13. [21]

    Neoantigens in cancer immunotherapy

    Ton N Schumacher and Robert D Schreiber. Neoantigens in cancer immunotherapy. Science, 348(6230):69–74, 2015

    2015

  14. [22]

    Deeptcr is a deep learning framework for revealing sequence concepts within t-cell repertoires

    John-William Sidhom, H Benjamin Larman, Drew M Pardoll, and Alexander S Baras. Deeptcr is a deep learning framework for revealing sequence concepts within t-cell repertoires. Nature communications , 12(1):1605, 2021

    2021

  15. [23]

    Prediction of specific tcr-peptide binding from large dictionaries of tcr-peptide pairs

    Ido Springer, Hanan Besser, Nili Tickotsky-Moskovitz, Shirit Dvorkin, and Yoram Louzoun. Prediction of specific tcr-peptide binding from large dictionaries of tcr-peptide pairs. Frontiers in immunology , page 1803, 2020

    2020

  16. [24]

    A document-level neural machine translation model with dynamic caching guided by theme-rheme information

    Yiqi Tong, Jiangbin Zheng, Hongkang Zhu, Yidong Chen, and Xiaodong Shi. A document-level neural machine translation model with dynamic caching guided by theme-rheme information. In Proceedings of the 28th International Conference on Computational Linguistics , pages 4385– 4395, 2020

    2020

  17. [25]

    Immunogenicity of somatic mutations in human gastrointestinal cancers

    Eric Tran, Mojgan Ahmadzadeh, Yong-Chen Lu, Alena Gros, Simon Turcotte, Paul F Robbins, Jared J Gartner, Zhili Zheng, Yong F Li, Satyajit Ray, et al. Immunogenicity of somatic mutations in human gastrointestinal cancers. Science, 350(6266):1387–1390, 2015

    2015

  18. [26]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems , 30, 2017

    2017

  19. [27]

    Titan: T- cell receptor specificity prediction with bimodal attention networks

    Anna Weber, Jannis Born, and Mar ´ıa Rodriguez Mart ´ınez. Titan: T- cell receptor specificity prediction with bimodal attention networks. Bioinformatics, 37(Supplement 1):i237–i244, 2021

    2021

  20. [28]

    Fuzzy slic: Fuzzy simple linear iterative clustering

    Chong Wu, Jiangbin Zheng, Zhenan Feng, Houwang Zhang, Le Zhang, Jiawang Cao, and Hong Yan. Fuzzy slic: Fuzzy simple linear iterative clustering. IEEE Transactions on Circuits and Systems for Video Technology, 31(6):2114–2124, 2020

    2020

  21. [29]

    Discognn: A sample-efficient framework for self-supervised graph representation learning

    Jun Xia, Shaorong Chen, Yue Liu, Zhangyang Gao, Jiangbin Zheng, Xihong Yang, and Stan Z Li. Discognn: A sample-efficient framework for self-supervised graph representation learning. In 2024 IEEE 40th International Conference on Data Engineering (ICDE) , pages 2876–

    2024

  22. [30]

    Understand- ing the limitations of deep models for molecular property prediction: Insights and solutions

    Jun Xia, Lecheng Zhang, Xiao Zhu, Yue Liu, Zhangyang Gao, Bozhen Hu, Cheng Tan, Jiangbin Zheng, Siyuan Li, and Stan Z Li. Understand- ing the limitations of deep models for molecular property prediction: Insights and solutions. Advances in Neural Information Processing Systems...

    2024

  23. [31]

    Dlptcr: an ensemble deep learning framework for predicting immuno- genic peptide recognized by t cell receptor

    Zhaochun Xu, Meng Luo, Weizhong Lin, Guangfu Xue, Pingping Wang, Xiyun Jin, Chang Xu, Wenyang Zhou, Yideng Cai, Wenyi Yang, et al. Dlptcr: an ensemble deep learning framework for predicting immuno- genic peptide recognized by t cell receptor. Briefings in Bioinformatics , 22(6...

    2021

  24. [32]

    Investigation of antigen-specific t-cell receptor clusters in human cancerstumor-infiltrating antigen-specific tcr clusters

    Hongyi Zhang, Longchao Liu, Jian Zhang, Jiahui Chen, Jianfeng Ye, Sachet Shukla, Jian Qiao, Xiaowei Zhan, Hao Chen, Catherine J Wu, et al. Investigation of antigen-specific t-cell receptor clusters in human cancerstumor-infiltrating antigen-specific tcr clusters. Clinical Canc...

    2020

  25. [33]

    Giana allows computationally- efficient tcr clustering and multi-disease repertoire classification by isometric transformation

    Hongyi Zhang, Xiaowei Zhan, and Bo Li. Giana allows computationally- efficient tcr clustering and multi-disease repertoire classification by isometric transformation. Nature communications, 12(1):4699, 2021

    2021

  26. [34]

    High-throughput determination of the antigen specificities of t cell receptors in single cells

    Shu-Qi Zhang, Ke-Yue Ma, Alexandra A Schonnesen, Mingliang Zhang, Chenfeng He, Eric Sun, Chad M Williams, Weiping Jia, and Ning Jiang. High-throughput determination of the antigen specificities of t cell receptors in single cells. Nature biotechnology, 36(12):1156–1159, 2018

    2018

  27. [35]

    Enhancing neural sign language translation by highlighting the facial expression information

    Jiangbin Zheng, Yidong Chen, Chong Wu, Xiaodong Shi, and Suhail Muhammad Kamal. Enhancing neural sign language translation by highlighting the facial expression information. Neurocomputing, 464:462–472, 2021

    2021

  28. [36]

    Mmdesign: Multi- modality transfer learning for generative protein design

    Jiangbin Zheng, Siyuan Li, Yufei Huang, Zhangyang Gao, Cheng Tan, Bozhen Hu, Jun Xia, Ge Wang, and Stan Z Li. Mmdesign: Multi- modality transfer learning for generative protein design. arXiv preprint arXiv:2312.06297, 2023

    2023 arXiv

  29. [37]

    Leveraging graph-based cross-modal information fusion for neural sign language translation

    Jiangbin Zheng, Siyuan Li, Cheng Tan, Chong Wu, Yidong Chen, and Stan Z Li. Leveraging graph-based cross-modal information fusion for neural sign language translation. arXiv preprint arXiv:2211.00526, 2022

    2022 arXiv

  30. [38]

    Progressive multi-modality learning for inverse protein folding

    Jiangbin Zheng and Stan Z Li. Progressive multi-modality learning for inverse protein folding. In 2024 IEEE International Conference on Multimedia and Expo (ICME) , pages 1–6. IEEE, 2024

    2024

  31. [39]

    Lightweight contrastive protein structure-sequence transformation

    Jiangbin Zheng, Ge Wang, Yufei Huang, Bozhen Hu, Siyuan Li, Cheng Tan, Xinwen Fan, and Stan Z Li. Lightweight contrastive protein structure-sequence transformation. arXiv preprint arXiv:2303.11783 , 2023

    2023 arXiv

  32. [40]

    Cvt-slr: Contrastive visual-textual transformation for sign language recognition with variational alignment

    Jiangbin Zheng, Yile Wang, Cheng Tan, Siyuan Li, Ge Wang, Jun Xia, Yidong Chen, and Stan Z Li. Cvt-slr: Contrastive visual-textual transformation for sign language recognition with variational alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ...

    2023

  33. [41]

    Using context-to-vector with graph retrofitting to improve word embeddings

    Jiangbin Zheng, Yile Wang, Ge Wang, Jun Xia, Yufei Huang, Guojiang Zhao, Yue Zhang, and Stan Li. Using context-to-vector with graph retrofitting to improve word embeddings. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Vol- ume 1: ...

    2022

  34. [42]

    Metaenzyme: Meta pan-enzyme learning for task-adaptive redesign

    Jiangbin Zheng, Han Zhang, Qianqing Xu, An-Ping Zeng, and Stan Z Li. Metaenzyme: Meta pan-enzyme learning for task-adaptive redesign. In Proceedings of the 32nd ACM International Conference on Multimedia , pages 1721–1730, 2024

    2024

  35. [43]

    An improved sign language translation model with explainable adaptations for processing long sign sentences

    Jiangbin Zheng, Zheng Zhao, Min Chen, Jing Chen, Chong Wu, Yidong Chen, Xiaodong Shi, and Yiqi Tong. An improved sign language translation model with explainable adaptations for processing long sign sentences. Computational Intelligence and Neuroscience , 2020, 2020

    2020

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.