Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine

By Ryuji Hamamoto, Masaaki Komatsu, Ken Takasawa, Ken Asada, Syuzo Kaneko. 2019

100 citations recorded by the source corpus.

To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations have been actively conducted for a long time, and a large number of achievements have already been reported. Indeed, genomic medicine is considered the core discipline of precision medicine, and currently, the clinical application of cutting-edge genomic medicine aimed at improving the prevention, diagnosis and treatment of a wide range of diseases is promoted. However, although the Human Genome Project was completed in 2003 and large-scale genetic analyses have since been accomplished worldwide with the development of next-generation sequencing (NGS), explaining the mechanism of disease onset only using genetic variation has been recognized as difficult. Meanwhile, the importance of epigenetics, which describes inheritance by mechanisms other than the genomic DNA sequence, has recently attracted attention, and, in particular, many studies have reported the involvement of epigenetic deregulation in human cancer. So far, given that genetic and epigenetic studies tend to be accomplished independently, physiological relationships between genetics and epigenetics in diseases remain almost unknown. Since this situation may be a disadvantage to developing precision medicine, the integrated understanding of genetic variation and epigenetic deregulation appears to be now critical. Importantly, the current progress of artificial intelligence (AI) technologies, such as machine learning and deep learning, is remarkable and enables multimodal analyses of big omics data. In this regard, it is important to develop a platform that can conduct multimodal analysis of medical big data using AI as this may accelerate the realization of precision medicine. In this review, we discuss the importance of genome-wide epigenetic and multiomics analyses using AI in the era of precision medicine.

Subfields: Cancer Research, Molecular Biology

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Citations in the corpus

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020, background): “The rapid progress in machine learning technologies, especially deep learning, along with the development of information infrastructure technologies such as the graphics processing unit (GPU), and the development of public databases, have made it possible to make use of large scale data called big d”
Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data (2020, background): “This has implications for decision-making tools and our pipeline may reduce the cost of examination through elimination of unnecessary surveys and acceleration of routine omics analysis benefitting precision medicine [47].”
Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer (2020, background): “It suggests that multi-omics analysis, including epigenetic data, could improve availability of output, in terms of precision medicine or personalized medicine, as we recently reported [65].”
Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review (2024, background): “The popularity and utility of CNNs surged with the advent of deep learning and increased computing power in the 21st century [43].”