Identification of characteristic methylation sites in gastric cancer using genomics-based machine learning

VernacularTitle:基于胃癌基因组学的机器学习识别特征性甲基化位点
Author: Xiaojiang WANG ¹ ; Wei LIU ; Baozhen CHEN ; Yinzhu HE ; Yanping CHEN ; Gang CHEN
Author Information

1. 福建省肿瘤医院分子病理室，福州　350014
Keywords: Stomach neoplasms; Artificial intelligence; DNA methylation; Neural networks (computer)
From: Chinese Journal of Pathology 2021;50(4):363-368
CountryChina
Language:Chinese
Abstract: Objective:To construct a prediction model of gastric cancer related methylation using machine learning algorithms based on genomic data.Methods:The gene mutation data, gene expression data and methylation chip data of gastric cancer were downloaded from The Caner Genome Atlas database, feature selection was conducted, and support vector machine (radial basis function), random forest and error back propagation (BP) neural network models were constructed; the model was verified in the new data set.Results:Among the three machine learning models, BP neural network had the highest test efficiency (F1 score=0.89,Kappa=0.66, area under curve=0.93).Conclusion:Machine learning algorithms, particularly BP neural network, can be used to take advantages of the genomic data for discovering molecular markers, and to help identify characteristic methylation sites of gastric cancer.