LASSO-based machine learning algorithm to predict the incidence of diabetes in different stages

Ou, Qianying; Jin, Wei; Lin, Leweihua; Lin, Danhong; Chen, Kaining; Quan, Huibiao

doi:10.1080/13685538.2023.2205510

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…As a result, it is widely employed in the classification or feature selection of high‐dimensional data. 12 , 13 To our knowledge, a limited number of studies have combined WGCNA and LASSO regression to identify the key genes that reflect the effects of maternal obesity on the hypothalamic function of offspring.…”

Section: Introductionmentioning

confidence: 99%

“…The least absolute shrinkage and selection operator (LASSO) regression is a popular machine learning method known for its ability to effectively select important feature values with non‐zero coefficients through regularization. As a result, it is widely employed in the classification or feature selection of high‐dimensional data 12,13 . To our knowledge, a limited number of studies have combined WGCNA and LASSO regression to identify the key genes that reflect the effects of maternal obesity on the hypothalamic function of offspring.…”

Section: Introductionmentioning

confidence: 99%

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Identifying of immune‐associated genes for assessing the obesity‐associated risk to the offspring in maternal obesity: A bioinformatics and machine learning

Shang,

Wang,

et al. 2024

CNS Neurosci Ther

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BackgroundPerinatal exposure to maternal obesity predisposes offspring to develop obesity later in life. Immune dysregulation in the hypothalamus, the brain center governing energy homeostasis, is pivotal in obesity development. This study aimed to identify key candidate genes associated with the risk of offspring obesity in maternal obesity.MethodsWe obtained obesity‐related datasets from the Gene Expression Omnibus (GEO) database. GSE135830 comprises gene expression data from the hypothalamus of mouse offspring in a maternal obesity model induced by a high‐fat diet model (maternal high‐fat diet (mHFD) group and maternal chow (mChow) group), while GSE127056 consists of hypothalamus microarray data from young adult mice with obesity (high‐fat diet (HFD) and Chow groups). We identified differentially expressed genes (DEGs) and module genes using Limma and weighted gene co‐expression network analysis (WGCNA), conducted functional enrichment analysis, and employed a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to pinpoint candidate hub genes for diagnosing obesity‐associated risk in offspring of maternal obesity. We constructed a nomogram receiver operating characteristic (ROC) curve to evaluate the diagnostic value. Additionally, we analyzed immune cell infiltration to investigate immune cell dysregulation in maternal obesity. Furthermore, we verified the expression of the candidate hub genes both in vivo and in vitro.ResultsThe GSE135830 dataset revealed 2868 DEGs between the mHFD offspring and the mChow group and 2627 WGCNA module genes related to maternal obesity. The overlap of DEGs and module genes in the offspring with maternal obesity in GSE135830 primarily enriched in neurodevelopment and immune regulation. In the GSE127056 dataset, 133 DEGs were identified in the hypothalamus of HFD‐induced adult obese individuals. A total of 13 genes intersected between the GSE127056 adult obesity DEGs and the GSE135830 maternal obesity module genes that were primarily enriched in neurodevelopment and the immune response. Following machine learning, two candidate hub genes were chosen for nomogram construction. Diagnostic value evaluation by ROC analysis determined Sytl4 and Kncn2 as hub genes for maternal obesity in the offspring. A gene regulatory network with transcription factor–miRNA interactions was established. Dysregulated immune cells were observed in the hypothalamus of offspring with maternal obesity. Expression of Sytl4 and Kncn2 was validated in a mouse model of hypothalamic inflammation and a palmitic acid‐stimulated microglial inflammation model.ConclusionTwo candidate hub genes (Sytl4 and Kcnc2) were identified and a nomogram was developed to predict obesity risk in offspring with maternal obesity. These findings offer potential diagnostic candidate genes for identifying obesity‐associated risks in the offspring of obese mothers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying of immune‐associated genes for assessing the obesity‐associated risk to the offspring in maternal obesity: A bioinformatics and machine learning

Shang,

Wang,

et al. 2024

CNS Neurosci Ther

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Machine learning application in otology

Koyama

2024

Auris Nasus Larynx

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Machine learning for predicting hepatitis B or C virus infection in diabetic patients

Kim,

Park,

Lee

2023

Sci Rep

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Highly prevalent hepatitis B and hepatitis C virus (HBV and HCV) infections have been reported among individuals with diabetes. Given the frequently asymptomatic nature of hepatitis and the challenges associated with screening in some vulnerable populations such as diabetes patients, we conducted an investigation into the performance of various machine learning models for the identification of hepatitis in diabetic patients while also evaluating the significance of features. Analyzing NHANES data from 2013 to 2018, machine learning models were evaluated; random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and least absolute shrinkage and selection operator (LASSO) along with stacked ensemble model. We performed hyperparameter tuning to improve the performance of the model, and selected important predictors using the best performance model. LASSO showed the highest predictive performance (AUC-ROC = 0.810) rather than other models. Illicit drug use, poverty, and race were highly ranked as predictive factors for developing hepatitis in diabetes patients. Our study demonstrated that a machine-learning-based model performed optimally in the detection of hepatitis among diabetes patients, achieving high performance. Furthermore, models and predictors evaluated from the current study, we expect, could be supportive information for developing screening or treatment methods for hepatitis care in diabetes patients.

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LASSO-based machine learning algorithm to predict the incidence of diabetes in different stages

Cited by 4 publications

References 28 publications

Identifying of immune‐associated genes for assessing the obesity‐associated risk to the offspring in maternal obesity: A bioinformatics and machine learning

Identifying of immune‐associated genes for assessing the obesity‐associated risk to the offspring in maternal obesity: A bioinformatics and machine learning

Machine learning application in otology

Machine learning for predicting hepatitis B or C virus infection in diabetic patients

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