Research graph
References from Recent advances in unlocking T cell signatures in cancer through deep learning. Local targets link to admitted publications; unresolved targets remain external evidence.
Deep learning workflow in radiology: a primer
10.1186/s13244-019-0832-5 · 2020 · External reference
Unresolved reference
External reference
Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management
10.1186/s12938-025-01349-w · 2025 · External reference
International evaluation of an AI system for breast cancer screening
10.1038/s41586-019-1799-6 · 2020 · External reference
Unresolved reference
External reference
Current AI technologies in cancer diagnostics and treatment
10.1186/s12943-025-02369-9 · 2025 · External reference
AI-driven nanomedicine for cancer theranostics
10.1186/s12943-025-02563-9 · 2026 · External reference
Risk prediction with electronic health records: A deep learning approach
2016 · External reference
Improving healthcare decision-making with predictive analytics: A conceptual approach to patient risk assessment and care optimization
2024 · External reference
Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms
10.1186/s12911-021-01639-y · 2021 · External reference
A machine learning model for predicting risk of hospital readmission within 30 days of discharge: validated with LACE index and patient at risk of hospital readmission (PARR) model
10.1007/s11517-020-02165-1 · 2020 · External reference
Immune repertoires of de-identified TCGA tumor samples assembled by
2022 · External reference
Analysis of the repertoire features of TCR beta chain CDR3 in human by high-throughput sequencing
10.1159/000445656 · 2016 · External reference
TCR-BERT: learning the grammar of T-cell receptors for flexible antigen-binding analyses
2024 · External reference
TPepRet: a deep learning model for characterizing T-cell receptors-antigen binding patterns
10.1093/bioinformatics/btaf022 · 2024 · External reference
Deep learning-based prediction of the T cell receptor-antigen binding specificity
10.1038/s42256-021-00383-2 · 2021 · External reference
TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding
10.1016/j.csbj.2023.11.037 · 2024 · External reference
Structure-based prediction of T cell receptor:peptide-MHC interactions
10.7554/elife.82813 · 2023 · External reference
T cell recognition of tumor neoantigens and insights into T cell immunotherapy
10.3389/fimmu.2022.833017 · 2022 · External reference
Neoantigen-specific stimulation of tumor-infiltrating lymphocytes enables effective TCR isolation and expansion while preserving stem-like memory phenotypes
10.1136/jitc-2023-008645 · 2024 · External reference
The T cell receptor β chain repertoire of tumor infiltrating lymphocytes improves neoantigen prediction and prioritization
10.7554/elife.94658 · 2024 · External reference
TCR sequencing analysis of cancer tissues and tumor draining lymph nodes in colorectal cancer patients
10.1080/2162402x.2019.1588085 · 2019 · External reference
Circulating clonally expanded T cells reflect functions of tumor-infiltrating T cells
10.1084/jem.20200921 · 2021 · External reference
vivo labeling reveals continuous trafficking of TCF-1+ T cells between tumor and lymphoid tissue
10.1084/jem.20210749 · 2022 · External reference
Peripheral T cell expansion predicts tumour infiltration and clinical response
10.1038/s41586-020-2056-8 · 2020 · External reference
TCR sequencing: applications in immuno-oncology research
2023 · External reference
Spatial transcriptomics of B cell and T cell receptors reveals lymphocyte clonal dynamics
10.1126/science.adf8486 · 2023 · External reference
High-sensitive spatially resolved T cell receptor sequencing with SPTCR-seq
10.1038/s41467-023-43201-6 · 2023 · External reference
Single cell resolved spatial immune repertoire unveils spatial heterogeneity of lymphoid aggregates in human immune disorders
2025 · External reference
Single-cell immune repertoire analysis
10.1038/s41592-024-02243-4 · 2024 · External reference
De novo prediction of cancer-associated T cell receptors for noninvasive cancer detection
10.1126/scitranslmed.aaz3738 · 2020 · External reference
Bias in the αβ T-cell repertoire: implications for disease pathogenesis and vaccination
10.1038/icb.2010.139 · 2011 · External reference
BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire
10.1093/bib/bbae420 · 2024 · External reference
DeepLION: Deep multi-instance learning improves the prediction of cancer-associated T cell receptors for accurate cancer detection
2022 · External reference
Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences
10.1186/s12859-022-05012-2 · 2022 · External reference
DeepLION2: deep multi-instance contrastive learning framework enhancing the prediction of cancer-associated T cell receptors by attention strategy on motifs
10.3389/fimmu.2024.1345586 · 2024 · External reference
The deep learning framework iCanTCR enables early cancer detection using the T-cell receptor repertoire in peripheral blood
10.1158/0008-5472.can-23-0860 · 2024 · External reference
Peripheral blood TCR repertoire improves early detection across multiple cancer types utilizing a cancer predictor
2025 · External reference
AutoTFCNNY: A multi-instance neural network for enhanced early cancer detection using TCR data
2025 · External reference
Cancer risk assessment based on human immune repertoire and deep learning
10.1007/978-981-19-6901-0_70 · 2022 · External reference
Multiple instance learning method based on convolutional neural network and self-attention for early cancer detection
2024 · External reference
Parallel convolutional and Transformer encoder method for cancer related T cell receptor sequences prediction
10.1016/j.asoc.2025.113681 · 2025 · External reference
Multimodality based deep learning method for cancer-related T-cell receptor sequence prediction
2025 · External reference
Biophysicochemical motifs in T-cell receptor sequences distinguish repertoires from tumor-infiltrating lymphocyte and adjacent healthy tissue
10.1158/0008-5472.can-18-2292 · 2019 · External reference
Ultrasensitive detection of TCR hypervariable-region sequences in solid-tissue RNA-seq data
10.1038/ng.3820 · 2017 · External reference
The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge
2015 · External reference
Investigation of antigen-specific T-cell receptor clusters in human cancers
10.1158/1078-0432.ccr-19-3249 · 2020 · External reference
M. Kanehisa, AAindex: amino acid index database
10.1093/nar/28.1.374 · 2000 · External reference
TCR-seq identifies distinct repertoires of distant-metastatic and nondistant-metastatic thyroid tumors
10.1210/clinem/dgaa452 · 2020 · External reference
Lung cancer-associated T cell repertoire as potential biomarker for early detection of stage I lung cancer
10.1016/j.lungcan.2021.09.017 · 2021 · External reference
Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire
10.1038/ng.3822 · 2017 · External reference
Mapping the functional landscape of T cell receptor repertoires by single-T cell transcriptomics
10.1038/s41592-020-01020-3 · 2021 · External reference
Solving the protein sequence metric problem
10.1073/pnas.0408677102 · 2005 · External reference
Deep residual learning for image recognition
2016 · External reference
CD-HIT: accelerated for clustering the next-generation sequencing data
10.1093/bioinformatics/bts565 · 2012 · External reference
ProteinBERT: a universal deep-learning model of protein sequence and function
10.1093/bioinformatics/btac020 · 2022 · External reference
MiXCR: software for comprehensive adaptive immunity profiling
10.1038/nmeth.3364 · 2015 · External reference
International Nucleotide Sequence Database Collaboration, The sequence read archive
10.1093/nar/gkq1019 · 2011 · External reference
Dynamics of individual T cell repertoires: From cord blood to centenarians
10.4049/jimmunol.1600005 · 2016 · External reference
Evolutionary-scale prediction of atomic-level protein structure with a language model
10.1126/science.ade2574 · 2023 · External reference
ImageNet: A large-scale hierarchical image database
2009 · External reference
Systems-level immunomonitoring in children with solid tumors to enable precision medicine
10.1016/j.cell.2024.12.014 · 2025 · External reference
VDJdb: a curated database of T-cell receptor sequences with known antigen specificity
10.1093/nar/gkx760 · 2018 · External reference
Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning
10.1093/nar/gkaf025 · 2025 · External reference
Data leakage inflates prediction performance in connectome-based machine learning models
10.1038/s41467-024-46150-w · 2024 · External reference
Improved protein structure prediction using potentials from deep learning
10.1038/s41586-019-1923-7 · 2020 · External reference
Highly accurate protein structure prediction with AlphaFold
10.1038/s41586-021-03819-2 · 2021 · External reference
Accurate structure prediction of biomolecular interactions with AlphaFold 3
10.1038/s41586-024-07487-w · 2024 · External reference
Boltz-1 democratizing biomolecular interaction modeling
2025 · External reference
Boltz-2: Towards accurate and efficient binding affinity prediction
2025 · External reference
Opportunities and obstacles for deep learning in biology and medicine
10.1098/rsif.2017.0387 · 2018 · External reference
Analytical evaluation of the clonoSEQ Assay for establishing measurable (minimal) residual disease in acute lymphoblastic leukemia, chronic lymphocytic leukemia, and multiple myeloma
10.1186/s12885-020-07077-9 · 2020 · External reference
Cancer risk assessment based on human immune repertoire and deep learning
10.1007/978-981-19-6901-0_70 · ExternalCitation · doi-reference
A machine learning model for predicting risk of hospital readmission within 30 days of discharge: validated with LACE index and patient at risk of hospital readmission (PARR) model
10.1007/s11517-020-02165-1 · ExternalCitation · doi-reference
Parallel convolutional and Transformer encoder method for cancer related T cell receptor sequences prediction
10.1016/j.asoc.2025.113681 · ExternalCitation · doi-reference
Systems-level immunomonitoring in children with solid tumors to enable precision medicine
10.1016/j.cell.2024.12.014 · ExternalCitation · doi-reference
TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding
10.1016/j.csbj.2023.11.037 · ExternalCitation · doi-reference
Lung cancer-associated T cell repertoire as potential biomarker for early detection of stage I lung cancer
10.1016/j.lungcan.2021.09.017 · ExternalCitation · doi-reference
Bias in the αβ T-cell repertoire: implications for disease pathogenesis and vaccination
10.1038/icb.2010.139 · ExternalCitation · doi-reference
Ultrasensitive detection of TCR hypervariable-region sequences in solid-tissue RNA-seq data
10.1038/ng.3820 · ExternalCitation · doi-reference
Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire
10.1038/ng.3822 · ExternalCitation · doi-reference
MiXCR: software for comprehensive adaptive immunity profiling
10.1038/nmeth.3364 · ExternalCitation · doi-reference
High-sensitive spatially resolved T cell receptor sequencing with SPTCR-seq
10.1038/s41467-023-43201-6 · ExternalCitation · doi-reference
Data leakage inflates prediction performance in connectome-based machine learning models
10.1038/s41467-024-46150-w · ExternalCitation · doi-reference
International evaluation of an AI system for breast cancer screening
10.1038/s41586-019-1799-6 · ExternalCitation · doi-reference
Improved protein structure prediction using potentials from deep learning
10.1038/s41586-019-1923-7 · ExternalCitation · doi-reference
Peripheral T cell expansion predicts tumour infiltration and clinical response
10.1038/s41586-020-2056-8 · ExternalCitation · doi-reference
Highly accurate protein structure prediction with AlphaFold
10.1038/s41586-021-03819-2 · ExternalCitation · doi-reference
Accurate structure prediction of biomolecular interactions with AlphaFold 3
10.1038/s41586-024-07487-w · ExternalCitation · doi-reference
Mapping the functional landscape of T cell receptor repertoires by single-T cell transcriptomics
10.1038/s41592-020-01020-3 · ExternalCitation · doi-reference
Single-cell immune repertoire analysis
10.1038/s41592-024-02243-4 · ExternalCitation · doi-reference
Deep learning-based prediction of the T cell receptor-antigen binding specificity
10.1038/s42256-021-00383-2 · ExternalCitation · doi-reference
Solving the protein sequence metric problem
10.1073/pnas.0408677102 · ExternalCitation · doi-reference
TCR sequencing analysis of cancer tissues and tumor draining lymph nodes in colorectal cancer patients
10.1080/2162402x.2019.1588085 · ExternalCitation · doi-reference
Circulating clonally expanded T cells reflect functions of tumor-infiltrating T cells
10.1084/jem.20200921 · ExternalCitation · doi-reference
vivo labeling reveals continuous trafficking of TCF-1+ T cells between tumor and lymphoid tissue
10.1084/jem.20210749 · ExternalCitation · doi-reference
BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire
10.1093/bib/bbae420 · ExternalCitation · doi-reference
ProteinBERT: a universal deep-learning model of protein sequence and function
10.1093/bioinformatics/btac020 · ExternalCitation · doi-reference
TPepRet: a deep learning model for characterizing T-cell receptors-antigen binding patterns
10.1093/bioinformatics/btaf022 · ExternalCitation · doi-reference
CD-HIT: accelerated for clustering the next-generation sequencing data
10.1093/bioinformatics/bts565 · ExternalCitation · doi-reference
M. Kanehisa, AAindex: amino acid index database
10.1093/nar/28.1.374 · ExternalCitation · doi-reference
Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning
10.1093/nar/gkaf025 · ExternalCitation · doi-reference
International Nucleotide Sequence Database Collaboration, The sequence read archive
10.1093/nar/gkq1019 · ExternalCitation · doi-reference
VDJdb: a curated database of T-cell receptor sequences with known antigen specificity
10.1093/nar/gkx760 · ExternalCitation · doi-reference
Opportunities and obstacles for deep learning in biology and medicine
10.1098/rsif.2017.0387 · ExternalCitation · doi-reference
Evolutionary-scale prediction of atomic-level protein structure with a language model
10.1126/science.ade2574 · ExternalCitation · doi-reference
Spatial transcriptomics of B cell and T cell receptors reveals lymphocyte clonal dynamics
10.1126/science.adf8486 · ExternalCitation · doi-reference
De novo prediction of cancer-associated T cell receptors for noninvasive cancer detection
10.1126/scitranslmed.aaz3738 · ExternalCitation · doi-reference
Neoantigen-specific stimulation of tumor-infiltrating lymphocytes enables effective TCR isolation and expansion while preserving stem-like memory phenotypes
10.1136/jitc-2023-008645 · ExternalCitation · doi-reference
Biophysicochemical motifs in T-cell receptor sequences distinguish repertoires from tumor-infiltrating lymphocyte and adjacent healthy tissue
10.1158/0008-5472.can-18-2292 · ExternalCitation · doi-reference
The deep learning framework iCanTCR enables early cancer detection using the T-cell receptor repertoire in peripheral blood
10.1158/0008-5472.can-23-0860 · ExternalCitation · doi-reference
Investigation of antigen-specific T-cell receptor clusters in human cancers
10.1158/1078-0432.ccr-19-3249 · ExternalCitation · doi-reference
Analysis of the repertoire features of TCR beta chain CDR3 in human by high-throughput sequencing
10.1159/000445656 · ExternalCitation · doi-reference
Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences
10.1186/s12859-022-05012-2 · ExternalCitation · doi-reference
Analytical evaluation of the clonoSEQ Assay for establishing measurable (minimal) residual disease in acute lymphoblastic leukemia, chronic lymphocytic leukemia, and multiple myeloma
10.1186/s12885-020-07077-9 · ExternalCitation · doi-reference
Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms
10.1186/s12911-021-01639-y · ExternalCitation · doi-reference
Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management
10.1186/s12938-025-01349-w · ExternalCitation · doi-reference
Current AI technologies in cancer diagnostics and treatment
10.1186/s12943-025-02369-9 · ExternalCitation · doi-reference
AI-driven nanomedicine for cancer theranostics
10.1186/s12943-025-02563-9 · ExternalCitation · doi-reference
Deep learning workflow in radiology: a primer
10.1186/s13244-019-0832-5 · ExternalCitation · doi-reference
TCR-seq identifies distinct repertoires of distant-metastatic and nondistant-metastatic thyroid tumors
10.1210/clinem/dgaa452 · ExternalCitation · doi-reference
T cell recognition of tumor neoantigens and insights into T cell immunotherapy
10.3389/fimmu.2022.833017 · ExternalCitation · doi-reference
DeepLION2: deep multi-instance contrastive learning framework enhancing the prediction of cancer-associated T cell receptors by attention strategy on motifs
10.3389/fimmu.2024.1345586 · ExternalCitation · doi-reference
Dynamics of individual T cell repertoires: From cord blood to centenarians
10.4049/jimmunol.1600005 · ExternalCitation · doi-reference
Structure-based prediction of T cell receptor:peptide-MHC interactions
10.7554/elife.82813 · ExternalCitation · doi-reference
The T cell receptor β chain repertoire of tumor infiltrating lymphocytes improves neoantigen prediction and prioritization
10.7554/elife.94658 · ExternalCitation · doi-reference
