ArticleFrontiers in aging neuroscience2022
Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review.Journal of Alzheimer's disease : JAD · 2024Pooled it
- Alzheimer's disease prediction using deep learning and XAI based interpretable feature selection from blood gene expression data.Scientific reports · 2026Article
- From proteome-wide Mendelian randomization and multi-omics integration to functional validation: TGFB3 as a prioritized candidate in gastric adenocarcinoma.Frontiers in oncology · 2026Article
- Decoding Pathogenic Mutational Landscapes in Alzheimer's Disease Through Integrated Transcriptomics.Human mutation · 2026Article
- Identification and Analysis of Potential Biomarkers Associated with Neutrophil Extracellular Traps in Cervicitis.Biochemical genetics · 2025Article
- Systematic Identification of Mitochondrial Signatures in Alzheimer's Disease and Inflammatory Bowel Disease.Molecular neurobiology · 2025Article
- Development and validation of machine learning nomograms for predicting survival in stage IV pancreatic cancer: A retrospective study.World journal of gastrointestinal oncology · 2025Article
- Colony Site Selection of Gray Heron (Ecology and evolution · 2025Article
- Review
- Integrative Multi-omics Analysis to Characterize Herpes Virus Infection Increases the Risk of Alzheimer's Disease.Molecular neurobiology · 2024Article
- GSDME-mediated pyroptosis promotes anti-tumor immunity of neoadjuvant chemotherapy in breast cancer.Cancer immunology, immunotherapy : CII · 2024Article
- Construction of a diagnostic model based on random forest and artificial neural network for peri-implantitis.Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2024Article
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- Integrated Bioinformatics Analysis Reveals Diagnostic Biomarkers and Immune Cell Infiltration Characteristics of Solar Lentigines.Clinical, cosmetic and investigational dermatology · 2024Article
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- Identification of Lipocalin 2 as a Ferroptosis-Related Key Gene Associated with Hypoxic-Ischemic Brain Damage via STAT3/NF-κB Signaling Pathway.Antioxidants (Basel, Switzerland) · 2023Article
- Construction of a neural network diagnostic model for renal fibrosis and investigation of immune infiltration characteristics.Frontiers in immunology · 2023Article
- Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.Frontiers in genetics · 2023Article
- Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis.Frontiers in genetics · 2022Article
- Establishment and analysis of a disease risk prediction model for the systemic lupus erythematosus with random forest.Frontiers in immunology · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a neurodegenerative condition that causes cognitive decline over time. Because existing diagnostic approaches for AD are limited, improving upon previously established diagnostic models based on genetic biomarkers is necessary. Firstly, four AD gene expression datasets were collected from the Gene Expression Omnibus (GEO) database. Two datasets were used to establish diagnostic models, and the other two datasets were used to verify the model effect. We merged GSE5281 with GSE44771 as the training dataset and found 120 DEGs. Then, we used random forest (RF) to screen 6 key genes (KLF15, MAFF, ITPKB, SST, DDIT4, and NRXN3) as being critical for separating AD and normal samples. The weights of these key genes were measured, and a diagnostic model was created using an artificial neural network (ANN). The area under the curve (AUC) of the model is 0.953, while the accuracy is 0.914. In the final step, two validation datasets were utilized to assess AUC performance. In GSE109887, our model had an AUC of 0.854, and in GSE132903, it had an AUC of 0.810. To summarize, we successfully identified key gene biomarkers and developed a new AD diagnostic model.
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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 Socratic graph.