BRIDGE-AD reveals Alzheimer's disease effectors through interpretable large-scale omics integration
- 1University of Cambridge
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, https://explore-bridgead.com, enables users to trace the curated evidence and generate mechanistic hypotheses.
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BibTeX
@misc{cerneckis2026bridgead,
title = {{BRIDGE-AD reveals Alzheimer's disease effectors through interpretable large-scale omics integration}},
author = {J. Cerneckis and G. Baltusyte and H. Convey and G. Sun and Z. C. E. Abela and M. Ramirez and D. Wang and G. Sun and T. Zhou and D. Spring and K. Saeb-Parsy and N. Han and Y. Shi},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.14.750802},
url = {https://www.biorxiv.org/content/10.64898/2026.09.14.750802v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - BRIDGE-AD reveals Alzheimer's disease effectors through interpretable large-scale omics integration
AU - J. Cerneckis
AU - G. Baltusyte
AU - H. Convey
AU - G. Sun
AU - Z. C. E. Abela
AU - M. Ramirez
AU - D. Wang
AU - G. Sun
AU - T. Zhou
AU - D. Spring
AU - K. Saeb-Parsy
AU - N. Han
AU - Y. Shi
PY - 2026
DA - 2026/09/14
PB - bioRxiv
DO - 10.64898/2026.09.14.750802
UR - https://www.biorxiv.org/content/10.64898/2026.09.14.750802v1
AB - 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, https://explore-bridgead.com, enables users to trace the curated evidence and generate mechanistic hypotheses.
ER -