Data Sheet 1_Alzheimer’s disease diagnosis based on gene selection and cross-tissue validation.pdf

However, traditional clinical diagnostic techniques, such as invasive cerebrospinal fluid punctures and expensive neuroimaging, struggle to meet the demands of large-scale early screening.</p>Methods<p>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.</p>Results<p>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.</p>Conclusion<p>DeepFS not only exhibits competitive performance in AD diagnostic classification, but can also serve as a valuable prior reference for future AD biomarker research.</p>