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• W3140490971 abstract "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." @default.
• W3140490971 created "2021-04-13" @default.
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• W3140490971 date "2021-03-25" @default.
• W3140490971 modified "2023-10-14" @default.
• W3140490971 title "A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset" @default.
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• W3140490971 doi "https://doi.org/10.3389/fgene.2021.644378" @default.
• W3140490971 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8044773" @default.
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• W3140490971 hasPublicationYear "2021" @default.
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