---
title: "BRIDGE-AD: Interpretable multimodal omics integration identifies Alzheimer's disease effector genes"
id: "biorxiv-11-bridge-ad-reveals-alzheimer-s-disease-effectors-through-interpretable-large"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-11-bridge-ad-reveals-alzheimer-s-disease-effectors-through-interpretable-large"
content_type: "clinical_feed_article"
specialty: "Neurology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.14.750802v1?rss=1"
published_at: "2026-09-20T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# BRIDGE-AD: Interpretable multimodal omics integration identifies Alzheimer's disease effector genes
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-11-bridge-ad-reveals-alzheimer-s-disease-effectors-through-interpretable-large
- **Specialty:** [Neurology](https://medichelpline.com/clinical-feed/neurology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.14.750802v1?rss=1)
- **Published At:** 2026-09-20T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The study introduces **BRIDGE-AD**, an interpretable network medicine framework that converts multimodal datasets into a unified, disease-specific gene representation to prioritise Alzheimer's disease (AD) effectors. - The authors integrated over 30 datasets and curated resources spanning omics, functional assays, genetics, and prior disease knowledge layers to build the resource. - BRIDGE-AD outperformed recently published pretrained and modality-specific gene embeddings at recovering known AD-associated genes according to the authors' benchmarking. - The framework produced a genome-wide list of candidate AD effectors and grouped established and newly prioritised genes into 19 functional clusters, mapping a global molecular landscape of AD biology. - Results supported an **SPP1**-centred cross-compartment hypothesis in AD and nominated **SCARB2** as a poorly characterised candidate for experimental follow-up. - Functional validation data reported that SCARB2 altered lysosomal, lipid-handling, and autophagic programmes in microglia; altered SCARB2 glycosylation in AD suggested disrupted SCARB2 processing and function. - The authors provide an interactive website (explore-bridgead.com) to trace curated evidence and to help generate mechanistic hypotheses from the integrated resource. - Code and data are available via a GitHub repository linked in the manuscript (https://github.com/Greta-B/BRIDGE-AD). - Funding sources included the National Institute on Aging (NIH grants R01 AG072291, R01 AG079307), NIHR Cambridge Biomedical Research Centre, National Research Foundation of Korea, Brain Pool Plus Fellowship Program, CIRM training support, and others. - One competing interest disclosed: Namshik Han has industry affiliations unrelated to this manuscript; other authors declared no competing interests.
## Clinical Analysis & Structured Key Points
BRIDGE-AD reveals Alzheimer's disease effectors through interpretable large-scale omics integration | bioRxiv Skip to main content New Results BRIDGE-AD reveals Alzheimer's disease effectors through interpretable large-scale omics integration Jonas Cerneckis , Greta Baltusyte , Harry Convey , Guoqiang Sun , Zyrille C. E. Abela , Miguel Ramirez , Daniel Wang , Guihua Sun , Tao Zhou , David Spring , Kourosh Saeb-Parsy , Namshik Han , Yanhong Shi doi: https://doi.org/10.64898/2026.09.14.750802 Jonas Cerneckis 1 Beckman Research Institute of City of Hope; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Greta Baltusyte 2 University of Cambridge Find this author on Google Scholar Find this author on PubMed Search for this author on this site Harry Convey 2 University of Cambridge Find this author on Google Scholar Find this author on PubMed Search for this author on this site Guoqiang Sun 1 Beckman Research Institute of City of Hope; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zyrille C. E. Abela 1 Beckman Research Institute of City of Hope; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Miguel Ramirez 1 Beckman Research Institute of City of Hope; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Wang 1 Beckman Research Institute of City of Hope; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Guihua Sun 1 Beckman Research Institute of City of Hope; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tao Zhou 1 Beckman Research Institute of City of Hope; Find this author on Google Scholar Find this author on PubMed Search for this author on this site David Spring 2 University of Cambridge Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kourosh Saeb-Parsy 2 University of Cambridge Find this author on Google Scholar Find this author on PubMed Search for this author on this site Namshik Han 2 University of Cambridge Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: nh417{at}cam.ac.uk Yanhong Shi 1 Beckman Research Institute of City of Hope; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Info/History Metrics Supplementary material Data/Code Preview PDF Abstract The growing landscape of Alzheimer's disease (AD) datasets creates opportunities to integrate heterogeneous evidence and systematically discover disease effectors. We present BRIDGE-AD, an interpretable network medicine framework that transforms multimodal data into a unified, disease-specific gene representation for AD effector prioritisation. We integrated more than 30 datasets and curated resources spanning omics, functional, genetic and prior disease knowledge layers. BRIDGE-AD outperformed recently published pretrained and modality-specific gene embeddings in recovering AD-associated genes and produced a genome-wide resource of candidate AD effectors. Established and newly prioritised effectors formed 19 functional clusters, revealing a global molecular landscape of AD biology. BRIDGE-AD supported an SPP1-centred cross-compartment hypothesis and nominated SCARB2, a poorly characterised candidate, for functional validation. SCARB2 rewired lysosomal, lipid-handling and autophagic programmes in microglia, whereas disrupted SCARB2 glycosylation in AD implicated altered SCARB2 processing and function. The accompanying website, explore-bridgead.com, enables users to trace the curated evidence and generate mechanistic hypotheses. Competing Interest Statement Namshik Han is the co-founder and Chief Technology Officer of CardiaTec Bio, a company developing therapeutics for cardiovascular diseases, and the co-founder of KURE.ai, which focuses on AI-driven oncology drug discovery. Namshik Han also serves on the Scientific Advisory Board of the Institute of Cancer Research (ICR). These affiliations are unrelated to the subject matter of this manuscript. The other authors declare no competing interests. Footnotes https://explore-bridgead.com https://github.com/Greta-B/BRIDGE-AD Funder Information Declared National Institute on Aging of the National Institutes of Health , R01 AG072291 , R01 AG079307 NIHR Cambridge Biomedical Research Centre , BRC-1215-20014 National Research Foundation of Korea grant funded by the Ministry of Science and ICT , RS-2025-18362970 Brain Pool Plus Fellowship Program funded by the Ministry of Science and ICT , RS-2025-25427881 Standigm Stem Cell Biology and Regenerative Medicine Research Training Program of the California Institute for Regenerative Medicine (CIRM) , EDUC4-12772 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Back to top Previous Next Posted September 20, 2026. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following BRIDGE-AD reveals Alzheimer's disease effectors through interpretable large-scale omics integration Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share BRIDGE-AD reveals Alzheimer's disease effectors through interpretable large-scale omics integration Jonas Cerneckis , Greta Baltusyte , Harry Convey , Guoqiang Sun , Zyrille C. E. Abela , Miguel Ramirez , Daniel Wang , Guihua Sun , Tao Zhou , David Spring , Kourosh Saeb-Parsy , Namshik Han , Yanhong Shi bioRxiv 2026.09.14.750802; doi: https://doi.org/10.64898/2026.09.14.750802 Share This Article: Copy Citation Tools BRIDGE-AD reveals Alzheimer's disease effectors through interpretable large-scale omics integration Jonas Cerneckis , Greta Baltusyte , Harry Convey , Guoqiang Sun , Zyrille C. E. Abela , Miguel Ramirez , Daniel Wang , Guihua Sun , Tao Zhou , David Spring , Kourosh Saeb-Parsy , Namshik Han , Yanhong Shi bioRxiv 2026.09.14.750802; doi: https://doi.org/10.64898/2026.09.14.750802 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Areas All Articles Animal Behavior and Cognition (8013) Biochemistry (18739) Bioengineering (14888) Bioinformatics (44418) Biophysics (22599) Cancer Biology (19723) Cell Biology (26899) Clinical Trials (138) Developmental Biology (13965) Ecology (21005) Epidemiology (2067) Evolutionary Biology (25455) Genetics (16166) Genomics (23507) Immunology (18705) Microbiology (42503) Molecular Biology (18059) Neuroscience (93451) Paleontology (700) Pathology (2977) Pharmacology and Toxicology (5095) Physiology (8114) Plant Biology (15999) Scientific Communication and Education (2095) Synthetic Biology (4560) Systems Biology (10235) Zoology (2391)
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