Abstract
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.
Highlights
Barack Obama, the 44th president of the United States, stated his intention to fund an amount of$215 million to the “Precision Medicine Initiative” in his 2015 State of the Union Address [1]
Deep learning is a type of machine leaning technique that aims at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower level features
This model is based on the idea that we can first learn or provide a distributed representation for each input feature, and learn how to map a feature’s distributed representation to the vector of parameters specific to that feature in the classifier neural network [169], which could deal with the issues of producing the parameters associated with each feature as a multitask learning model [169]
Summary
Barack Obama, the 44th president of the United States, stated his intention to fund an amount of. Most precision medicine interventions consist of genetic profiling, including the most precision medicine interventions consist of genetic profiling, including the detection of predictive individual patients In this model, diagnostic is often employed forthis selecting appropriate and detection of predictive biomarkers [3]. PeopleCancer can benefit benefit the from targeted drugs [4,6] These results indicate that we definitely need to explore the gene These sequencing of indicate. OmicsThese data,results such as epigenetic anddefinitely proteomics shouldthe be benefit from indicate that we needdata, to explore other omics data, such as epigenetic and proteomics data, should be involved, and integrated analyses possibility that moreanalyses patients of candifferent benefit from precision medicine. Data in precision medicine by describing, in particular, the integrated analysis of multiomics data, including epigenetic data, using machine learning and deep learning technologies
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