Author Correction: DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires
| Autor: | John-William Sidhom, Drew M. Pardoll, H. Benjamin Larman, Alexander S. Baras |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
Science T-Lymphocytes T cell Adaptive immunity Receptors Antigen T-Cell General Physics and Astronomy Computational biology General Biochemistry Genetics and Molecular Biology Functional clustering Mice Deep Learning Text mining Machine learning Databases Genetic Immunogenetics medicine Computational models Animals Humans Amino Acid Sequence RNA-Seq Author Correction Sequence (medicine) Multidisciplinary business.industry Published Erratum Deep learning High-Throughput Nucleotide Sequencing General Chemistry medicine.anatomical_structure Neural Networks Computer Artificial intelligence business Algorithms |
| Zdroj: | Nature Communications Nature Communications, Vol 12, Iss 1, Pp 1-1 (2021) |
| ISSN: | 2041-1723 |
| Popis: | Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system and allows for modeling its sequence determinants of antigenicity. We present DeepTCR, a suite of unsupervised and supervised deep learning methods able to model highly complex TCR sequencing data by learning a joint representation of a TCR by its CDR3 sequences and V/D/J gene usage. We demonstrate the utility of deep learning to provide an improved 'featurization' of the TCR across multiple human and murine datasets, including improved classification of antigen-specific TCRs and extraction of antigen-specific TCRs from noisy single-cell RNA-Seq and T-cell culture-based assays. Our results highlight the flexibility and capacity for deep neural networks to extract meaningful information from complex immunogenomic data for both descriptive and predictive purposes. |
| Databáze: | OpenAIRE |
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