Context-Aware Amino Acid Embedding Advances Analysis of TCR-Epitope Interactions
暂无分享,去创建一个
[1] Heewook Lee,et al. ATM-TCR: TCR-Epitope Binding Affinity Prediction Using a Multi-Head Self-Attention Model , 2022, Frontiers in immunology.
[2] Howard Y. Chang,et al. TCR-BERT: learning the grammar of T-cell receptors for flexible antigen-xbinding analyses , 2021, bioRxiv.
[3] John V. Heymach,et al. Deep learning-based prediction of the T cell receptor–antigen binding specificity , 2021, Nature Machine Intelligence.
[4] B. Peters,et al. NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data , 2021, Communications Biology.
[5] Xiaowei Zhan,et al. GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation , 2021, Nature Communications.
[6] M. Linial,et al. ProteinBERT: a universal deep-learning model of protein sequence and function , 2021, bioRxiv.
[7] H. Lähdesmäki,et al. Predicting recognition between T cell receptors and epitopes with TCRGP , 2021, PLoS Comput. Biol..
[8] Wout Bittremieux,et al. Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification , 2020, Briefings Bioinform..
[9] Jennifer N. Dines,et al. A large-scale database of T-cell receptor beta (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2 , 2020, Research square.
[10] B. Rost,et al. ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing , 2020, bioRxiv.
[11] J. Blankson,et al. A T Cell Receptor Sequencing-Based Assay Identifies Cross-Reactive Recall CD8+ T Cell Clonotypes Against Autologous HIV-1 Epitope Variants , 2020, Frontiers in Immunology.
[12] D. Ghersi,et al. Epstein-Barr Virus Epitope–Major Histocompatibility Complex Interaction Combined with Convergent Recombination Drives Selection of Diverse T Cell Receptor α and β Repertoires , 2020, mBio.
[13] B. Rost,et al. Modeling aspects of the language of life through transfer-learning protein sequences , 2019, BMC Bioinformatics.
[14] Sofie Gielis,et al. Detection of Enriched T Cell Epitope Specificity in Full T Cell Receptor Sequence Repertoires , 2019, Front. Immunol..
[15] Colin Raffel,et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , 2019, J. Mach. Learn. Res..
[16] Kevin Gimpel,et al. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations , 2019, ICLR.
[17] I. Springer,et al. Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs , 2019, bioRxiv.
[18] Huanming Yang,et al. PIRD: Pan immune repertoire database , 2018, bioRxiv.
[19] Zachary Wu,et al. Learned protein embeddings for machine learning , 2018, Bioinformatics.
[20] William S. DeWitt,et al. A Diverse Lipid Antigen–Specific TCR Repertoire Is Clonally Expanded during Active Tuberculosis , 2018, The Journal of Immunology.
[21] G. Mortier,et al. Memory CD4+ T cell receptor repertoire data mining as a tool for identifying cytomegalovirus serostatus , 2018, Genes & Immunity.
[22] Jaime Prilusky,et al. McPAS‐TCR: a manually curated catalogue of pathology‐associated T cell receptor sequences , 2017, Bioinform..
[23] Andrew K. Sewell,et al. VDJdb: a curated database of T-cell receptor sequences with known antigen specificity , 2017, Nucleic Acids Res..
[24] P. Bradley,et al. Quantifiable predictive features define epitope-specific T cell receptor repertoires , 2017, Nature.
[25] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[26] Kenji Doya,et al. Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning , 2017, Neural Networks.
[27] James M. Hogan,et al. Distributed Representations for Biological Sequence Analysis , 2016, ArXiv.
[28] Ehsaneddin Asgari,et al. Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics , 2015, PloS one.
[29] Alexander M. Rush,et al. Character-Aware Neural Language Models , 2015, AAAI.
[30] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[31] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[32] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[33] Quoc V. Le,et al. Distributed Representations of Sentences and Documents , 2014, ICML.
[34] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[35] Andrew K. Sewell,et al. Why must T cells be cross-reactive? , 2012, Nature Reviews Immunology.
[36] Mark M Davis,et al. How T cells 'see' antigen , 2005, Nature Immunology.
[37] C. Wülfing,et al. T cell receptor (TCR) clustering in the immunological synapse integrates TCR and costimulatory signaling in selected T cells. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[38] Joydeep Ghosh,et al. Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..
[39] T. Schumacher,et al. T-cell-receptor gene therapy , 2002, Nature Reviews Immunology.
[40] H. Sbai,et al. Use of T cell epitopes for vaccine development. , 2001, Current drug targets. Infectious disorders.
[41] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[42] S. Henikoff,et al. Amino acid substitution matrices from protein blocks. , 1992, Proceedings of the National Academy of Sciences of the United States of America.
[43] Mark M. Davis,et al. T-cell antigen receptor genes and T-cell recognition , 1988, Nature.
[44] J. H. Ward. Hierarchical Grouping to Optimize an Objective Function , 1963 .
[45] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[46] Michael I. Jordan,et al. Advances in Neural Information Processing Systems 30 , 1995 .