Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology.
Ken Asada,Syuzo Kaneko,Ken Takasawa,Hidenori Machino,Satoshi Takahashi,Norio Shinkai,Ryo Shimoyama,Masaaki Komatsu,Ryuji Hamamoto +8 more
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In this article, the authors focus on whole genome sequencing (WGS) analysis and epigenome analysis, introduce the latest results of omics analysis using ML technologies for the development of precision oncology, and discuss the future prospects.Abstract:
With the completion of the International Human Genome Project, we have entered what is known as the post-genome era, and efforts to apply genomic information to medicine have become more active. In particular, with the announcement of the Precision Medicine Initiative by U.S. President Barack Obama in his State of the Union address at the beginning of 2015, "precision medicine," which aims to divide patients and potential patients into subgroups with respect to disease susceptibility, has become the focus of worldwide attention. The field of oncology is also actively adopting the precision oncology approach, which is based on molecular profiling, such as genomic information, to select the appropriate treatment. However, the current precision oncology is dominated by a method called targeted-gene panel (TGP), which uses next-generation sequencing (NGS) to analyze a limited number of specific cancer-related genes and suggest optimal treatments, but this method causes the problem that the number of patients who benefit from it is limited. In order to steadily develop precision oncology, it is necessary to integrate and analyze more detailed omics data, such as whole genome data and epigenome data. On the other hand, with the advancement of analysis technologies such as NGS, the amount of data obtained by omics analysis has become enormous, and artificial intelligence (AI) technologies, mainly machine learning (ML) technologies, are being actively used to make more efficient and accurate predictions. In this review, we will focus on whole genome sequencing (WGS) analysis and epigenome analysis, introduce the latest results of omics analysis using ML technologies for the development of precision oncology, and discuss the future prospects.read more
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Targeting DNA Damage Response and Repair to Enhance Therapeutic Index in Cisplatin-Based Cancer Treatment.
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Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging.
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Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics.
Ken Asada,Masaaki Komatsu,Ryo Shimoyama,Ken Takasawa,Norio Shinkai,Akira Sakai,Amina Bolatkan,Masayoshi Yamada,Satoshi Takahashi,Hidenori Machino,Kazuma Kobayashi,Syuzo Kaneko,Ryuji Hamamoto +12 more
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Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research
Ken Asada,Ken Takasawa,Hidenori Machino,Satoshi Takahashi,Norio Shinkai,Amina Bolatkan,Kazuma Kobayashi,Masaaki Komatsu,Syuzo Kaneko,Koji Okamoto,Ryuji Hamamoto +10 more
TL;DR: In this paper, the authors present a comprehensive introduction to the implementation of machine learning techniques in medical research for single-cell analysis, and discuss their usefulness and future potential for singlecell analysis.
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Artificial Intelligence and Circulating Cell-Free DNA Methylation Profiling: Mechanism and Detection of Alzheimer’s Disease
Ray O. Bahado-Singh,Uppala Radhakrishna,Juozas Gordevičius,Buket Aydas,Ali Yilmaz,Faryal Jafar,Khaled Imam,Michael E. Maddens,Kshetra Challapalli,Raghu Metpally,Wade H. Berrettini,Richard C. Crist,Stewart F. Graham,Sangeetha Vishweswaraiah +13 more
TL;DR: This is the first reported genome-wide DNA methylation study using cfDNA to detect Alzheimer’s disease and describes numerous epigenetically altered genes which were previously reported to be differentially expressed in the brain of AD sufferers.
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