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  • Open Access


    A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

    Noureldin Eissa1,2,*, Uswah Khairuddin1,3, Rubiyah Yusof1, Ahmed Madani2

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2817-2838, 2023, DOI:10.32604/cmc.2023.033632

    Abstract Epigenetics is the study of phenotypic variations that do not alter DNA sequences. Cancer epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human cancers. One of these alterations is DNA methylation; an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in cancer. Therefore, studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA sequences. Currently, microarray technologies; such as Illumina Infinium BeadChip assays; are used to study DNA methylation at an extremely large number… More >

  • Open Access


    Microenvironment Analysis of Prognosis and Molecular Signature of Immune-Related Genes in Lung Adenocarcinoma

    Bo Ling, Zuliang Huang, Suoyi Huang, Li Qian, Genliang Li, Qianli Tang

    Oncology Research, Vol.28, No.6, pp. 561-578, 2020, DOI:10.3727/096504020X15907428281601

    Abstract There is growing evidence on the clinical significance of tumor microenvironment (TME) cells in predicting prognosis and therapeutic effects. However, cell interactions in tumor microenvironments have not been thoroughly studied or systematically analyzed so far. In this study, 22 immune cell components in the lung adenocarcinoma (LUAD) TME were analyzed using gene expression profile from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The TME-based molecular subtypes of LUAD were defined to evaluate further the relationship between molecular subtypes, prognosis, and clinical characteristics. A TME risk score model was constructed by using the differentially expressed genes (DEGs) of… More >

  • Open Access


    Racial Bias Can Confuse AI for Genomic Studies

    Beifen Dai1,#, Zhihao Xu2,#, Hongjue Li3, Bo Wang3, Jinsong Cai1, Xiaomo Liu4,*

    Oncologie, Vol.24, No.1, pp. 113-130, 2022, DOI:10.32604/oncologie.2022.020259

    Abstract Large-scale genomic studies are important ways to comprehensively decode the human genomics, and provide valuable insights to human disease causalities and phenotype developments. Genomic studies are in need of high throughput bioinformatics analyses to harness and integrate such big data. It is in this overarching context that artificial intelligence (AI) offers enormous potentials to advance genomic studies. However, racial bias is always an important issue in the data. It is usually due to the accumulation process of the dataset that inevitability involved diverse subjects with different races. How can race bias affect the outcomes of AI methods? In this work,… More >

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