{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"Research Square"}],"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T21:45:32Z","timestamp":1773351932059,"version":"3.50.1"},"posted":{"date-parts":[[2025,9,17]]},"group-title":"In Review","reference-count":0,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,9,9]]},"abstract":"<title>Abstract<\/title>\n        <p>Understanding complex diseases requires models that can integrate diverse layers of biological data while yielding insights that are biologically interpretable. Although multi-omics integration with machine learning (ML) has advanced disease prediction and biomarker discovery, most existing approaches overlook the hierarchical and regulatory relationships that connect these molecular layers. Here, we present the Hierarchical Input Neural Network (HINN), a deep learning framework that incorporates known cross-omics relationships directly into its architecture, capturing the flow of information from genomics to epigenomics, transcriptomics, and downstream biological processes. By embedding these relationships, HINN improves both predictive performance and biological interpretability.\n\nWe applied HINN to blood-derived multi-omics data from individuals with Alzheimer\u2019s disease or mild cognitive impairment to predict cognitive scores from standardized assessments. HINN outperformed both baseline and state-of-the-art models and pinpointed multi-omics biomarkers\u2014including SNPs and promoter-region CpG sites in ATP6V1C1 and RCHY1 that were significantly correlated with plasma p-Tau181 levels. These features map to biologically relevant processes with potential implications for cognitive decline.\n\nOur findings demonstrate how combining deep learning with biological knowledge can uncover interpretable, blood-based biomarkers for cognitive decline due to complex diseases such as Alzheimer\u2019s. All code and data are openly available at https:\/\/github.com\/bozdaglab\/HINN.<\/p>","DOI":"10.21203\/rs.3.rs-7576397\/v1","type":"posted-content","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T08:03:57Z","timestamp":1758096237000},"source":"Crossref","is-referenced-by-count":1,"title":["HINN: Hierarchical Input Neural Network identifies multi-omics biomarker for cognitive decline"],"prefix":"10.21203","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9599-9411","authenticated-orcid":false,"given":"Yashu","family":"Vashishath","sequence":"first","affiliation":[{"name":"University of North Texas"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah","family":"Beaver","sequence":"additional","affiliation":[{"name":"University of North Texas"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fahad","family":"Saeed","sequence":"additional","affiliation":[{"name":"Florida International University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4813-4310","authenticated-orcid":false,"given":"Serdar","family":"Bozdag","sequence":"additional","affiliation":[{"name":"University of North Texas"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","container-title":[],"original-title":[],"link":[{"URL":"https:\/\/www.researchsquare.com\/article\/rs-7576397\/v1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.researchsquare.com\/article\/rs-7576397\/v1.html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T08:03:57Z","timestamp":1758096237000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.researchsquare.com\/article\/rs-7576397\/v1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,17]]},"references-count":0,"URL":"https:\/\/doi.org\/10.21203\/rs.3.rs-7576397\/v1","relation":{},"subject":[],"published":{"date-parts":[[2025,9,17]]},"subtype":"preprint"}}