Analysis of super-enhancer using machine learning and its application to medical biology
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- Ryuji Hamamoto
- Division Chief in the Division of Medical AI Research and Development, National Cancer Center Research Institute; a Professor in the Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University and a Team Leader of the Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project
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- Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project and an External Research Staff in the Medical AI Research and Development, National Cancer Center Research Institute
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- Norio Shinkai
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University
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- Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project and an External Research Staff in the Medical AI Research and Development, National Cancer Center Research Institute
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- Nobuji Kouno
- Department of Surgery, Graduate School of Medicine, Kyoto University
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- Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project and an External Research Staff of Medical AI Research and Development, National Cancer Center Research Institute
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- Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project and an External Research Staff of Medical AI Research and Development, National Cancer Center Research Institute
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- Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute and a Visiting Scientist in the Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project
抄録
<jats:title>Abstract</jats:title> <jats:p>The analysis of super-enhancers (SEs) has recently attracted attention in elucidating the molecular mechanisms of cancer and other diseases. SEs are genomic structures that strongly induce gene expression and have been reported to contribute to the overexpression of oncogenes. Because the analysis of SEs and integrated analysis with other data are performed using large amounts of genome-wide data, artificial intelligence technology, with machine learning at its core, has recently begun to be utilized. In promoting precision medicine, it is important to consider information from SEs in addition to genomic data; therefore, machine learning technology is expected to be introduced appropriately in terms of building a robust analysis platform with a high generalization performance. In this review, we explain the history and principles of SE, and the results of SE analysis using state-of-the-art machine learning and integrated analysis with other data are presented to provide a comprehensive understanding of the current status of SE analysis in the field of medical biology. Additionally, we compared the accuracy between existing machine learning methods on the benchmark dataset and attempted to explore the kind of data preprocessing and integration work needed to make the existing algorithms work on the benchmark dataset. Furthermore, we discuss the issues and future directions of current SE analysis.</jats:p>
収録刊行物
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- Briefings in Bioinformatics
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Briefings in Bioinformatics 24 (3), 2023-03-23
Oxford University Press (OUP)
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詳細情報 詳細情報について
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- CRID
- 1360861707157312768
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- ISSN
- 14774054
- 14675463
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- データソース種別
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- Crossref
- KAKEN