Abstract Artificial intelligence (AI) has contributed substantially to the resolution of a variety of biomedical problems, including cancer, over the past decade. Deep learning, a sub-field of AI that is highly flexible and supports automatic feature extraction, is increasingly being applied in various areas of both basic and clinical cancer research. In this review, we describe numerous recent examples of the application of AI in oncology, including cases in which deep learning has efficiently solved problems that were previously thought to be unsolvable, and we address obstacles that must be overcome before such application can become more widespread. We also highlight resources and datasets that can help harness the power of AI for cancer research. The development of innovative approaches to and applications of AI will yield important insights in oncology in the coming decade.KEYWORDSartificial intelligence, deep learning, machine learning, oncology, personalized medicine
cer on the basis of genome-wide sequence data. Given that cancer is a complex disease, it is preferable to integrate More and more clinical cancer genomic data are gradually being multilayered data. The application of AI to analysis of a large amount accumulated. In 2017, the US FDA approved several genome seof omics (exome, transcriptome, and epigenome) data as well as data on the susceptibility of patients with acute myeloid leukemia to quence-based panels related to oncology, including the Oncomine anticancer drugs resulted in the identification of drug-susceptibility genes.47 Watson for Genomics also analyzed 323 patients to identify Dx Target Test, the Praxis Extended RAS Panel, MSK-IMPACT and
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FoundationOne CDx. In Japan, the Ministry of Health, Labor, and genomic alterations with potential clinical effects that were not recognized by the conventional Molecular Tumors panel.48 Welfare set a goal in May 2019 to perform genome-wide sequencing of 100 000 individuals over the next 3 years through the full-scale introDeep learning is also applied to the variant calling process. In duction of genome-wide sequencing to the clinic. AI is, thus, set to play addition to the standard variant detection framework, Googles
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DeepVariant exploits the Inception TensorFlow framework, which Machine learning also has the potential to provide novel biologiwas initially developed for image classification. This workflow cal insights. As one example, a regulatory role for Fbxw7 (one of the most frequently mutated E3 ubiquitin ligases in cancer) in the oxiconverts variant calling into an image recognition task by changing the BAM file into an image similar to the genome browser snapshot and determining variants based on likelihood.49 dative metabolism of cancer cells was discovered with the use of a machine learning algorithm (kernelized Bayesian transfer learning).51 In combination with our previous studies showing that Fbxw7 plays Another algorithm, ExPecto, links genetic mutations with disease prediction.50 ExPecto predicts the level of gene expression in each
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AI promoter-proximal sequences. This framework was built with the use of all publicly available genome-wide association studies and has can also precisely predict RNA splicing. Precursor mRNA transcripts been experimentally validated. Estimation of gene expression level undergo splicing to generate multiple mature mRNA isoforms, and SHIMIZU and naKaYaMa 13497006, 2020, 5, Downloaded from by Turkey Cochrane Evidence Aid, Wiley Online Library on [10/01/2024]. See the Terms and Conditions ( on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 1457
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