2023
DOI: 10.3389/fendo.2022.1079465
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Identification of ULK1 as a novel mitophagy-related gene in diabetic nephropathy

Abstract: BackgroundAccumulating evidence indicates that mitophagy is crucial for the development of diabetic nephropathy (DN). However, little is known about the key genes involved. The present study is to identify the potential mitophagy-related genes (MRGs) in DN.MethodsFive datasets were obtained from the Gene Expression Omnibus (GEO) database and were split into the training and validation set. Then the differentially expressed MRGs were screened and further analyzed for GO and KEGG enrichment. Next, three algorith… Show more

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Cited by 9 publications
(7 citation statements)
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References 70 publications
(66 reference statements)
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“…Capsular contracture is a common and severe complication after breast prosthesis implantation, 19 and its diagnosis and grading are predominantly based on the Baker grading system, which is a subjective system. The identification of biological markers of diseases using machine learning algorithms has been performed for many diseases, such as coronary artery disease, 20 diabetic nephropathy, 21 and essential thrombocythemia. 22 It is reasonable to consider DEGs as possible prognostic markers for the clinical classification of breast capsular contracture.…”
Section: Discussionmentioning
confidence: 99%
“…Capsular contracture is a common and severe complication after breast prosthesis implantation, 19 and its diagnosis and grading are predominantly based on the Baker grading system, which is a subjective system. The identification of biological markers of diseases using machine learning algorithms has been performed for many diseases, such as coronary artery disease, 20 diabetic nephropathy, 21 and essential thrombocythemia. 22 It is reasonable to consider DEGs as possible prognostic markers for the clinical classification of breast capsular contracture.…”
Section: Discussionmentioning
confidence: 99%
“…As a dimension reduction technique, the LASSO regression demonstrates superior performance in evaluating high-dimensional data as compared with regression analysis. It employs regularization to enhance prediction accuracy [ 26 ]. R package “glmnet” were applied for LASSO, which was performed by 10-fold cross-validation to adjust the optimal penalty parameter λ [ 27 ].…”
Section: Methodsmentioning
confidence: 99%
“…The SVM-RFE algorithm systematically ranks features recursively, effectively minimizing the risk of model overfitting [16][17][18]. We utilized the e1071 package in R, incorporating 10-fold cross-validation approach, which was meticulously set up using a corresponding seed to ensure reproducibility and to mitigate potential biases in model training.…”
Section: Support Vector Machines-recursive Feature Elimination (Svm-r...mentioning
confidence: 99%