首页|Shanxi Medical University Reports Findings in Bioinformatics (Screening core gen es for minimal change disease based on bioinformatics and machine learning appro aches)
Shanxi Medical University Reports Findings in Bioinformatics (Screening core gen es for minimal change disease based on bioinformatics and machine learning appro aches)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Biotechnology - Bioinf ormatics is the subject of a report. According to news originating from Taiyuan, People’s Republic of China, by NewsRx correspondents, research stated, “Based o n bioinformatics and machine learning methods, we conducted a study to screen th e core genes of minimal change disease (MCD) and further explore its pathogenesi s. First, we obtained the chip data sets GSE108113 and GSE200828 from the Gene E xpression Comprehensive Database (GEO), which contained MCD information.” Our news journalists obtained a quote from the research from Shanxi Medical Univ ersity, “We then used R software to analyze the gene chip data and performed fun ctional enrichment analysis. Subsequently, we employed Cytoscape to screen the c ore genes and utilized machine learning algorithms (random forest and LASSO regr ession) to accurately identify them. To validate and analyze the core genes, we conducted immunohistochemistry (IHC) and gene set enrichment analysis (GSEA). Ou r results revealed a total of 394 highly expressed differential genes. Enrichmen t analysis indicated that these genes are primarily involved in T cell different iation and p13k-akt signaling pathway of immune response. We identified NOTCH1, TP53, GATA3, and TGF-b1 as the core genes. IHC staining demonstrated significant differences in the expression of these four core genes between the normal group and the MCD group. Furthermore, GSEA suggested that their up-regulation may be closely associated with the pathological changes in MCD kidneys, particularly in the glycosaminoglycans signaling pathway.”
TaiyuanPeople’s Republic of ChinaAsi aBioinformaticsBiotechnologyCyborgsEmerging TechnologiesGeneticsInfo rmation TechnologyMachine Learning
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