[Machine Learning Applications in Cancer Genome Medicine]

Practical cancer genome medicine requires large-scale data analysis for many types of biological data such as cancer driver mutations, aberrantly methylated regions, gene expression also biological knowledge from literature. Machine learning algorithms play an important role in bioinformatics of clinical oncology. In this review, we examine the applications of machine learning algorithm on recent research of cancer genome medicine. As an introduction, we consider relationship between artificial intelligence(AI)and machine learning and characterize the technical advantages of deep learning. The later part of this article examines 4 research publications within 3 domains. That includes comprehensive research about actionable mutations, novel approach for identifying activated Ras pathway and feasible methodologies to increase sensitivity of detecting cancer with cell-free DNA from 2 different research groups.