Machine Learning Approaches to TCR Repertoire Analysis
Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.858057/full |
| _version_ | 1818005517594787840 |
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| author | Yotaro Katayama Ryo Yokota Taishin Akiyama Taishin Akiyama Tetsuya J. Kobayashi Tetsuya J. Kobayashi |
| author_facet | Yotaro Katayama Ryo Yokota Taishin Akiyama Taishin Akiyama Tetsuya J. Kobayashi Tetsuya J. Kobayashi |
| author_sort | Yotaro Katayama |
| collection | DOAJ |
| description | Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects. |
| first_indexed | 2024-04-14T04:45:14Z |
| format | Article |
| id | doaj.art-2397c81a1f2244a6905d69ee8786e0df |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-14T04:45:14Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-2397c81a1f2244a6905d69ee8786e0df2022-12-22T02:11:28ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-07-011310.3389/fimmu.2022.858057858057Machine Learning Approaches to TCR Repertoire AnalysisYotaro Katayama0Ryo Yokota1Taishin Akiyama2Taishin Akiyama3Tetsuya J. Kobayashi4Tetsuya J. Kobayashi5Graduate School of Engineering, The University of Tokyo, Tokyo, JapanNational Research Institute of Police Science, Kashiwa, Chiba, JapanLaboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, JapanGraduate School of Medical Life Science, Yokohama City University, Yokohama, JapanInstitute of Industrial Science, The University of Tokyo, Tokyo, JapanGraduate School of Engineering, The University of Tokyo, Tokyo, JapanSparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.https://www.frontiersin.org/articles/10.3389/fimmu.2022.858057/fullmachine learningdeep learningT cellT cell receptorimmunoinformatics |
| spellingShingle | Yotaro Katayama Ryo Yokota Taishin Akiyama Taishin Akiyama Tetsuya J. Kobayashi Tetsuya J. Kobayashi Machine Learning Approaches to TCR Repertoire Analysis Frontiers in Immunology machine learning deep learning T cell T cell receptor immunoinformatics |
| title | Machine Learning Approaches to TCR Repertoire Analysis |
| title_full | Machine Learning Approaches to TCR Repertoire Analysis |
| title_fullStr | Machine Learning Approaches to TCR Repertoire Analysis |
| title_full_unstemmed | Machine Learning Approaches to TCR Repertoire Analysis |
| title_short | Machine Learning Approaches to TCR Repertoire Analysis |
| title_sort | machine learning approaches to tcr repertoire analysis |
| topic | machine learning deep learning T cell T cell receptor immunoinformatics |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.858057/full |
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