ArticleBriefings in bioinformatics2024
BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Machine Learning of Personal Repertoires From Public T Cell Receptors.Immunological reviews · 2026Review
- DUET: a graph-based workflow for TCR-epitope prioritization and tumor-reactive T-cell identification.Briefings in bioinformatics · 2026Article
- Mapping the TCR landscape: computational tools empowering translational immunology and therapy design.Journal for immunotherapy of cancer · 2026Review
- Deciphering small sequence differences in T cell receptor-antigen pairing.Nature communications · 2026Article
- The research progress of gastric cancer vaccines: a narrative review.Translational cancer research · 2026Review
- Enhancing mutation impact prediction in protein-protein interactions through interpretable graph-based multi-level feature interactions.Bioinformatics (Oxford, England) · 2026Article
- Algorithm guided personalized T cell therapy: machine learning unlocks next generation TCR engineered immunotherapy.Pharmacological reports : PR · 2026Review
- Decoding the adaptive immune repertoire for disease prediction.Nature reviews. Rheumatology · 2025Article
- Elucidating the Role of the T Cell Receptor Repertoire in Myelodysplastic Neoplasms and Acute Myeloid Leukemia.Diseases (Basel, Switzerland) · 2025Review
- Peripheral blood TCR repertoire improves early detection across multiple cancer types utilizing a cancer predictor.Frontiers in oncology · 2025Article
- T-cell receptor dynamics in digestive system cancers: a multi-layer machine learning approach for tumor diagnosis and staging.Frontiers in immunology · 2025Article
- STAG-LLM: Predicting TCR-pHLA binding with protein language models and computationally generated 3D structures.Computational and structural biotechnology journal · 2025Article
- Adaptive Treatment of Metastatic Prostate Cancer Using Generative Artificial Intelligence.Clinical Medicine Insights. Oncology · 2025Review
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Authors and funding
9 authors.
Funding
Abstract
The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to forecast cancer-related immune responses. However, existing methods often overlook the intra- and inter-sequence interactions of T cell receptors (TCRs), limiting the development of sequence-based cancer-related immune status predictions. To address this challenge, we propose BertTCR, an innovative deep learning framework designed to predict cancer-related immune status using TCRs. BertTCR combines a pre-trained protein large language model with deep learning architectures, enabling it to extract deeper contextual information from TCRs. Compared to three state-of-the-art sequence-based methods, BertTCR improves the AUC on an external validation set for thyroid cancer detection by 21 percentage points. Additionally, this model was trained on over 2000 publicly available TCR libraries covering 17 types of cancer and healthy samples, and it has been validated on multiple public external datasets for its ability to distinguish cancer patients from healthy individuals. Furthermore, BertTCR can accurately classify various cancer types and healthy individuals. Overall, BertTCR is the advancing method for cancer-related immune status forecasting based on TCRs, offering promising potential for a wide range of immune status prediction tasks.
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