A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning
Satoshi Takahashi, Masamichi Takahashi, Shota Tanaka, Shunsaku Takayanagi, Hirokazu Takami, Erika Yamazawa, Shohei Nambu, Mototaka Miyake, Kaishi Satomi, Koichi Ichimura, Yoshitaka Narita, Ryuji Hamamoto
Abstract
Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient's quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.