首页|Data on Machine Learning Reported by Jianning Song and Colleagues (Comprehensive analysis of aging-related gene expression patterns and identification of potent ial intervention targets)
Data on Machine Learning Reported by Jianning Song and Colleagues (Comprehensive analysis of aging-related gene expression patterns and identification of potent ial intervention targets)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Guiyang, Peo ple’s Republic of China, by NewsRx correspondents, research stated, “This study aims to understand the molecular mechanisms underlying the aging process and ide ntify potential interventions to mitigate age-related decline and diseases. This study utilized the GSE168753 dataset to conduct comprehensive differential gene expression analysis and co-expression module analysis.” Our news editors obtained a quote from the research, “Machine learning and Mende lian randomization analyses were employed to identify core aging-associated gene s and potential drug targets. Molecular docking simulations and mediation analys is were also performed to explore potential compounds and mediators involved in the aging process. The analysis identified 4164 differentially expressed genes, with 1893 upregulated and 2271 downregulated genes. Co-expression analysis revea led 21 modules, including both positively and negatively correlated modules betw een older age and younger age groups. Further exploration identified 509 aging-r elated genes with distinct biological functions. Machine learning and Mendelian randomization analyses identified eight core genes associated with aging, includ ing DPP9, GNAZ, and RELL2. Molecular docking simulations suggested resveratrol, folic acid, and ethinyl estradiol as potential compounds capable of attenuating aging through modulation of RELL2 expression. Mediation analysis indicated that eosinophil counts and neutrophil count might act as mediators in the causal rela tionship between genes and aging-related indicators. This comprehensive study pr ovides valuable insights into the molecular mechanisms of aging and offers impor tant implications for the development of antiaging therapeutics. Key Messages W hat is already known on this topic - Prior research outlines aging’s complexity, necessitating precise molecular targets for intervention. What this study adds - This study identifies novel aging-related genes, potential drug targets, and t herapeutic compounds, advancing our understanding of aging mechanisms.”
GuiyangPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesGeneticsMachine Learning
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