Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers DOI Open Access
Kesheng Wang, Donald Adjeroh, Wei Fang

et al.

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(6), P. 2428 - 2428

Published: March 8, 2025

Mild cognitive impairment (MCI) is a clinical condition characterized by decline in ability and progression of impairment. It often considered transitional stage between normal aging Alzheimer’s disease (AD). This study aimed to compare deep learning (DL) traditional machine (ML) methods predicting MCI using plasma proteomic biomarkers. A total 239 adults were selected from the Disease Neuroimaging Initiative (ADNI) cohort along with pool 146 We evaluated seven ML models (support vector machines (SVMs), logistic regression (LR), naïve Bayes (NB), random forest (RF), k-nearest neighbor (KNN), gradient boosting (GBM), extreme (XGBoost)) six variations neural network (DNN) model—the DL model H2O package. Least Absolute Shrinkage Selection Operator (LASSO) 35 biomarkers pool. Based on grid search, DNN an activation function “Rectifier With Dropout” 2 layers 32 revealed best highest accuracy 0.995 F1 Score 0.996, while among methods, XGBoost was 0.986 0.985. Several correlated APOE-ε4 genotype, polygenic hazard score (PHS), three cerebrospinal fluid (Aβ42, tTau, pTau). Bioinformatics analysis Gene Ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG) several molecular functions pathways associated biomarkers, including cytokine-cytokine receptor interaction, cholesterol metabolism, regulation lipid localization. The results showed that may represent promising tool prediction MCI. These help early diagnosis, prognostic risk stratification, treatment interventions for individuals at

Language: Английский

Host Transcriptome and Microbial Variation in Relation to Visceral Hyperalgesia DOI Open Access
Christopher J. Costa, Stephanie Prescott, Nicolaas H. Fourie

et al.

Nutrients, Journal Year: 2025, Volume and Issue: 17(5), P. 921 - 921

Published: March 6, 2025

Chronic visceral hypersensitivity is associated with an overstressed pain response to noxious stimuli (hyperalgesia). Microbiota are active modulators of host biology and implicated in the etiology hypersensitivity. we studied association between circulating mRNA transcriptome, intensity induced (IVP), variation oral microbiome among participants without baseline Transcriptomic profiles microbial abundance were correlated IVP intensity. Host microbes explored, linking RNA biology. 259 OTUs found be through correlation differential expression 471 genes molecular pathways related inflammation neural mechanisms, including Rho PI3K/AKT pathways. The bacterial families Lachnospiraceae, Prevotellaceae, Veillonellaceae showed highest degree association. Oral reduced diversity characteristic Our results suggest that may involved systemic immune inflammatory effects play a role nervous system stem cell interactions hypersensitivity, differentially expressed pathways, microbiota described here provide framework for further work exploring relationship microbiome.

Language: Английский

Citations

0
Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers DOI Open Access
Kesheng Wang, Donald Adjeroh, Wei Fang

et al.

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(6), P. 2428 - 2428

Published: March 8, 2025

Mild cognitive impairment (MCI) is a clinical condition characterized by decline in ability and progression of impairment. It often considered transitional stage between normal aging Alzheimer’s disease (AD). This study aimed to compare deep learning (DL) traditional machine (ML) methods predicting MCI using plasma proteomic biomarkers. A total 239 adults were selected from the Disease Neuroimaging Initiative (ADNI) cohort along with pool 146 We evaluated seven ML models (support vector machines (SVMs), logistic regression (LR), naïve Bayes (NB), random forest (RF), k-nearest neighbor (KNN), gradient boosting (GBM), extreme (XGBoost)) six variations neural network (DNN) model—the DL model H2O package. Least Absolute Shrinkage Selection Operator (LASSO) 35 biomarkers pool. Based on grid search, DNN an activation function “Rectifier With Dropout” 2 layers 32 revealed best highest accuracy 0.995 F1 Score 0.996, while among methods, XGBoost was 0.986 0.985. Several correlated APOE-ε4 genotype, polygenic hazard score (PHS), three cerebrospinal fluid (Aβ42, tTau, pTau). Bioinformatics analysis Gene Ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG) several molecular functions pathways associated biomarkers, including cytokine-cytokine receptor interaction, cholesterol metabolism, regulation lipid localization. The results showed that may represent promising tool prediction MCI. These help early diagnosis, prognostic risk stratification, treatment interventions for individuals at

Language: Английский

Citations

0