首页|Anhui University of Chinese Medicine Reports Findings in Bioinformatics (Develop ment of a clinical prediction model for diabetic kidney disease with glucose and lipid metabolism disorders based on machine learning and bioinformatics technol ogy)
Anhui University of Chinese Medicine Reports Findings in Bioinformatics (Develop ment of a clinical prediction model for diabetic kidney disease with glucose and lipid metabolism disorders based on machine learning and bioinformatics technol ogy)
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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 Hefei, P eople's Republic of China, by NewsRx correspondents, research stated, "In this s tudy, we investigated the internal relationship between the pathogenesis of diab etic kidney disease (DKD) and abnormal glucose and lipid metabolism to identify potential biomarkers for diagnosis and treatment and investigated the role of th e immune microenvironment of glucose and lipid metabolism disorders in the occur rence and progression of DKD. The chip datasets GSE104948 and GSE96804 from the Gene Expression Common Database (GEO) were merged using the ‘lima' and ‘sva' sof tware packages in R Software (4.2.3), and the merged dataset was used as the val idation set." Our news journalists obtained a quote from the research from the Anhui Universit y of Chinese Medicine, "The intersection between the differential genes of DKD a nd the glucose and lipid metabolism genes in the MSigDB database was identified, and a nomogram of the incidence risk of DKD was built using three machine learn ing methods, namely LASSO regression, support vector machine (SVM), and random f orest (RF), to validate the accuracy of the prediction model. Immune scores were conducted using the unsupervised clustering method, and patients were divided i nto two subgroups. The two subgroups were screened for differential genes for en richment analysis. The differential genes of patients diagnosed with DKD were cl ustered into two gene subgroups for co-expression analysis. In this study, we ut ilized the Cytoscape software to construct a network of interactions among key g enes. Using machine learning, a diagnostic model was developed with G6PC and HSD 17B14 as key factors. Enrichment analysis and immune scoring demonstrated that t he development of DKD was related to the imbalance in the microenvironment broug ht about by glucose lipid metabolism disorders."
HefeiPeople's Republic of ChinaAsiaBioinformaticsBiomarkersBiotechnologyCyborgsDiabetic Kidney DiseaseDi agnostics and ScreeningEmerging TechnologiesGeneticsHealth and MedicineI nformation TechnologyKidney Diseases and ConditionsLipid Metabolism Disorder sMachine LearningNutritional and Metabolic Diseases and ConditionsSoftware
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