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A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset
被引:15
作者:
Zhang, Ge
[1
]
Xue, Zijing
[1
]
Yan, Chaokun
[1
]
Wang, Jianlin
[1
]
Luo, Huimin
[1
]
机构:
[1] Henan Univ, Sch Comp & Informat Engn, Kaifeng, Peoples R China
基金:
中国博士后科学基金;
中国国家自然科学基金;
关键词:
gastric cancer;
omics data;
biomarkers;
feature selection;
deep neural network;
machine learning;
FEATURE-SELECTION;
ALGORITHM;
CLASSIFICATION;
OPTIMIZATION;
BLADDER;
PSCA;
D O I:
10.3389/fgene.2021.644378
中图分类号:
Q3 [遗传学];
学科分类号:
071007 ;
090102 ;
摘要:
As one type of complex disease, gastric cancer has high mortality rate, and there are few effective treatments for patients in advanced stage. With the development of biological technology, a large amount of multiple-omics data of gastric cancer are generated, which enables computational method to discover potential biomarkers of gastric cancer. That will be very important to detect gastric cancer at earlier stages and thus assist in providing timely treatment. However, most of biological data have the characteristics of high dimension and low sample size. It is hard to process directly without feature selection. Besides, only using some omic data, such as gene expression data, provides limited evidence to investigate gastric cancer associated biomarkers. In this research, gene expression data and DNA methylation data are integrated to analyze gastric cancer, and a feature selection approach is proposed to identify the possible biomarkers of gastric cancer. After the original data are pre-processed, the mutual information (MI) is applied to select some top genes. Then, fold change (FC) and T-test are adopted to identify differentially expressed genes (DEG). In particular, false discover rate (FDR) is introduced to revise p_value to further screen genes. For chosen genes, a deep neural network (DNN) model is utilized as the classifier to measure the quality of classification. The experimental results show that the approach can achieve superior performance in terms of accuracy and other metrics. Biological analysis for chosen genes further validates the effectiveness of the approach.
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页数:11
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