Alzheimer’s disease diagnosis based on gene selection and cross-tissue validation

AI Summary

Introduces DeepFS, a transcriptomic diagnostic framework with automated gene selection to remove noise and identify core gene targets for Alzheimer's diagnosis.
Achieves high accuracy on brain transcriptomes: 89.45% accuracy and AUC 0.9647, and generalises to peripheral blood datasets.
High-weight genes validated by single-nucleus RNA-seq at single-cell resolution; DeepFS offers valuable priors for future AD biomarker research.

TL;DR (AI Generated)

METHODS: This study proposes a novel transcriptomic-based diagnostic framework named DeepFS, which features an automated gene selection module to filter out redundant noise and identify high-value core gene targets for diagnosis.

To further explore the methodological generalizability of the framework across different tissue types, we extended DeepFS to a peripheral blood transcriptomic dataset, where it consistently achieved reliable diagnostic classification performance.

Furthermore, regarding the brain tissue transcriptomic data, the high-weight genes driven by the gene selection module were integrated with AD brain single-nucleus RNA sequencing data for quantitative analysis at single-cell resolution, further validating the biological efficacy of the module.

Basic summary

Front Genet. 2026 Sep 28;17:1890236. doi: 10.3389/fgene.2026.1890236. eCollection 2026.

ABSTRACT

BACKGROUND: Early and accurate diagnosis is critical for intervening in the progression of Alzheimer’s disease (AD). However, traditional clinical diagnostic techniques, such as invasive cerebrospinal fluid punctures and expensive neuroimaging, struggle to meet the demands of large-scale early screening.

RESULTS: DeepFS achieved high-precision identification of AD, yielding an ac-curacy of 89.45% and an AUC value of 0.9647 on brain tissue transcriptomic data. To further explore the methodological generalizability of the framework across different tissue types, we extended DeepFS to a peripheral blood transcriptomic dataset, where it consistently achieved reliable diagnostic classification performance. Furthermore, regarding the brain tissue transcriptomic data, the high-weight genes driven by the gene selection module were integrated with AD brain single-nucleus RNA sequencing data for quantitative analysis at single-cell resolution, further validating the biological efficacy of the module.

CONCLUSION: DeepFS not only exhibits competitive performance in AD diagnostic classification, but can also serve as a valuable prior reference for future AD biomarker research.

PMID:42861391 | PMC:PMC13654589 | DOI:10.3389/fgene.2026.1890236

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