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기계학습 접근법에 기반한 유전자 선택 방법들에 대한 리뷰
A review of gene selection methods based on machine learning approaches
Korean J Appl Stat 2022;35(5):667-684
Published online October 31, 2022
© 2022 The Korean Statistical Society.

이하정a, 김재직1,a
Hajoung Leea, Jaejik Kim1,a

a성균관대학교 통계학과

aDepartment of Statistics, Sungkyunkwan University
1 Department of Statistics, Sungkyunkwan University, 25-2 Sungkyunkwan-ro, Jongno-Gu, Seoul 03063, Korea. E-mail: jaejik@skku.edu
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1F1A1072444).
Received August 18, 2022; Revised August 24, 2022; Accepted August 25, 2022.
Abstract
유전자 발현 데이터는 각 유전자에 대해 mRNA 양의 정도를 나타내고, 그러한 유전자 발현량에 대한 분석은 질병 발생에 대한 메커니즘을 이해하고 새로운 치료제와 치료 방법을 개발하는데 중요한 아이디어를 제공해오고 있다. 오늘날 DNA 마이크로어레이와 RNA-시퀀싱과 같은 고출력 기술은 수천 개의 유전자 발현량을 동시에 측정하는 것을 가능하게 하여 고차원성이라는 유전자 발현 데이터의 특징을 발생시켰다. 이러한 고차원성으로 인해 유전자 발현 데이터를 분석하기 위한 학습 모형들은 과적합 문제에 부딪히기 쉽고, 이를 해결하기 위해 차원 축소 또는 변수 선택 기술들이 사전 분석 단계로써 보통 사용된다. 특히, 사전 분석 단계에서 우리는 유전자 선택법을 이용하여 부적절하거나 중복된 유전자를 제거할 수 있고 중요한 유전자를 찾아낼 수도 있다. 현재까지 다양한 유전자 선택 방법들이 기계학습의 맥락에서 개발되어왔다. 본 논문에서는 기계학습 접근법을 사용하는 최근의 유전자 선택 방법들을 집중적으로 살펴보고자 한다. 또한, 현재까지 개발된 유전자 선택 방법들의 근본적인 문제점과 앞으로의 연구 방향에 대해 논의하고자 한다.
Gene expression data present the level of mRNA abundance of each gene, and analyses of gene expressions have provided key ideas for understanding the mechanism of diseases and developing new drugs and therapies. Nowadays high-throughput technologies such as DNA microarray and RNA-sequencing enabled the simultaneous measurement of thousands of gene expressions, giving rise to a characteristic of gene expression data known as high dimensionality. Due to the high-dimensionality, learning models to analyze gene expression data are prone to overfitting problems, and to solve this issue, dimension reduction or feature selection techniques are commonly used as a preprocessing step. In particular, we can remove irrelevant and redundant genes and identify important genes using gene selection methods in the preprocessing step. Various gene selection methods have been developed in the context of machine learning so far. In this paper, we intensively review recent works on gene selection methods using machine learning approaches. In addition, the underlying diculties with current gene selection methods as well as future research directions are discussed.
주요어 : 유전자 선택, 유전자 발현 데이터, 지도학습, 비지도학습
Keywords : gene selection, gene expression data, supervised learning, unsupervised learning
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