AI and Neurological Rare & Neuroimmune Diseases ── Parkinson's ── 2026-10-14

Alzheimer's, Parkinson's and frontotemporal dementia are hard to diagnose because the data are complex and variable. The study collected curated microarray datasets from NCBI GEO, extracted expressed genes with GEO2R and Limma, then trained and validated an SVM on them. The SVM showed very high precision and recall and distinguished disease-specific expression profiles, pointing to gene-based diagnostics. The evidence is a preclinical in silico analysis. — Image abstract — the whole article on one page (click to enlarge)

1 Parkinson's papers. Lead: Analysis of Differentially Expressed Genes in Alzheimer's, Parkinson's, and Frontotemporal Dementia Using Machine Learning Algorithm

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Parkinson's disease and parkinsonism: treatment, trials and AI

Analysis of Differentially Expressed Genes in Alzheimer's, Parkinson's, and Frontotemporal Dementia Using Machine Learning Algorithm
CRC Press eBooks · 2026-10-12 · Evidence: Preclinical (in silico analysis of public gene expression datasets)
Abstract
The brain diseases such as Fronto-Temporal Dementia (FTD), Parkinson&s;s (PD), and Alzheimer&s;s (AD) are neurodegenerative disease release the neuroinflammation and protein misfolding. However the diagnosis of such diseases ruins challenging due to complexity of the dat and variability of the data. The present study identifies molecular signatures by applying machine learning (ML) techniques by using curated microarray datasets. The datasets are collected from National Center for Biotechnology Information (NCBI ) Gene Expression Omnibus (GEO).Based on R based analysis on GEO (GEO2R) as well as Limma package, the expressed genes (|logFC| ≥ 0.5; adj.P.Val ≤ 0.05) were obtained. ML model with Support Vector Machine (SVM), was developed by training and validating these genes. SVM showed very high precision as well as recall. The heatmaps are ensured the data quality. The work underlines the capability of statistical biomarkers and collective learning to distinguish between disease-specific gene expression profiles, henceforth enabling the development of gene-based diagnostics. In future, to enhance the interpretability of the model and improvement of the model, genomic structural data can be incorporated. This helps to identify the abnormal genes for the further conclusion.
Journal IF: unknown (could not be matched)
Reference: Manohar Mathkunti N, Rangaswamy S, Varsha VP, Bhat S. Analysis of Differentially Expressed Genes in Alzheimer's, Parkinson's, and Frontotemporal Dementia Using Machine Learning Algorithm. Interdisciplinary Approaches to Sustainable Research and Development in Science, Technology, and Socio-Economic Studies. 2026 Oct 12 [Epub ahead of print]. doi:10.1201/9781042035762-123.
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