ReviewScientific reports2023
Exploiting machine learning models to identify novel Alzheimer's disease biomarkers and potential targets.
Review in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 58 citations in OpenAlex.
- LASSO-HHO two-stage hybrid gene selection framework for accurate Alzheimer's disease diagnosis.Scientific reports · 2026Article
- Systematic review of unveiling the potential of AI using machine learning and deep learning methods in neurodegenerative diseases.PeerJ. Computer science · 2026Review
- A retrospective clinical analysis of inappropriate Xiangdan Injection use: clinical risk profiling and computational hypothesis generation.Frontiers in pharmacology · 2026Article
- A Scoping Review of Machine Learning-Based Prediction of Alzheimer's Disease Using Blood Biomarkers.Biomedical engineering and computational biology · 2026Review
- Deep Learning-Based Mpox Skin Lesion Detection and Real-Time Monitoring in a Smart Healthcare System.Diagnostics (Basel, Switzerland) · 2025Article
- Brain Stroke Classification Using CT Scans with Transformer-Based Models and Explainable AI.Diagnostics (Basel, Switzerland) · 2025Article
- Identification and experimental validation of biomarkers associated with the endocannabinoid system in major depressive disorder.Hereditas · 2025Article
- Machine Learning Framework for Ovarian Cancer Diagnostics Using Plasma Lipidomics and Metabolomics.International journal of molecular sciences · 2025Article
- Advancing Alzheimer's Therapy: Computational strategies and treatment innovations.IBRO neuroscience reports · 2025Review
- Integrative single-cell RNA sequencing and mendelian randomization analysis reveal the potential role of synaptic vesicle cycling-related genes in Alzheimer's disease.The journal of prevention of Alzheimer's disease · 2025Article
- Exploring the Role of microRNAs as Blood Biomarkers in Alzheimer's Disease and Frontotemporal Dementia.International journal of molecular sciences · 2025Review
- Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer's disease biomarkers.Fluids and barriers of the CNS · 2025Article
- Biomarker Identification for Alzheimer's Disease Using a Multi-Filter Gene Selection Approach.International journal of molecular sciences · 2025Article
- Effective Analysis of Alzheimer's Disease and Mechanisms of Methyl-4- Hydroxybenzoate using Network Toxicology, Molecular Docking, and Machine Learning Strategies.Current Alzheimer research · 2025Article
- A review of AI-based radiogenomics in neurodegenerative disease.Frontiers in big data · 2025Review
- Comparison of light gradient boosting and logistic regression for interactomic hub genes inFrontiers in oral health · 2025Article
- Article
- Nutritional strategies against dementia in rural populations.Frontiers in nutrition · 2025Review
- Efficient Explainable Models for Alzheimer's Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning.Diagnostics (Basel, Switzerland) · 2024Article
- AlzGenPred - CatBoost-based gene classifier for predicting Alzheimer's disease using high-throughput sequencing data.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We still do not have an effective treatment for Alzheimer's disease (AD) despite it being the most common cause of dementia and impaired cognitive function. Thus, research endeavors are directed toward identifying AD biomarkers and targets. In this regard, we designed a computational method that exploits multiple hub gene ranking methods and feature selection methods with machine learning and deep learning to identify biomarkers and targets. First, we used three AD gene expression datasets to identify 1/ hub genes based on six ranking algorithms (Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Betweenness Centrality (BC), Closeness Centrality, and Stress Centrality), 2/ gene subsets based on two feature selection methods (LASSO and Ridge). Then, we developed machine learning and deep learning models to determine the gene subset that best distinguishes AD samples from the healthy controls. This work shows that feature selection methods achieve better prediction performances than the hub gene sets. Beyond this, the five genes identified by both feature selection methods (LASSO and Ridge algorithms) achieved an AUC = 0.979. We further show that 70% of the upregulated hub genes (among the 28 overlapping hub genes) are AD targets based on a literature review and six miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and one transcription factor, JUN, are associated with the upregulated hub genes. Furthermore, since 2020, four of the six microRNA were also shown to be potential AD targets. To our knowledge, this is the first work showing that such a small number of genes can distinguish AD samples from healthy controls with high accuracy and that overlapping upregulated hub genes can narrow the search space for potential novel targets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.