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- W4360804057 abstract "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." @default.
- W4360804057 created "2023-03-25" @default.
- W4360804057 creator A5006914306 @default.
- W4360804057 creator A5011852177 @default.
- W4360804057 creator A5019504867 @default.
- W4360804057 creator A5020057840 @default.
- W4360804057 creator A5057196702 @default.
- W4360804057 creator A5062170991 @default.
- W4360804057 creator A5087732261 @default.
- W4360804057 creator A5090831621 @default.
- W4360804057 date "2023-03-23" @default.
- W4360804057 modified "2023-09-23" @default.
- W4360804057 title "Analysis of super-enhancer using machine learning and its application to medical biology" @default.
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