ArticleNature communications2021
Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer's disease neuropathologies.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Meta-Analysis of Transcriptomic Studies of Blood and Six Brain Regions Identifies a Consensus of 15 Cross-Tissue Mechanisms in Alzheimer's Disease and Suggests an Origin of Cross-Study Heterogeneity.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2025Pooled it
- scVIP: personalized modeling of single-cell transcriptomes for developmental and disease phenotypes.bioRxiv : the preprint server for biology · 2026Article
- Transparency of medical artificial intelligence systems.Nature reviews bioengineering · 2026Article
- Article
- Deep profiling of gene expression across 18 human cancers.Nature biomedical engineering · 2025Article
- Computational reconstruction of evolutionary selection in human brain networks.Frontiers in neuroinformatics · 2025Article
- A deep profile of gene expression across 18 human cancers.bioRxiv : the preprint server for biology · 2024Article
- Predicting Alzheimer's Cognitive Resilience Score: A Comparative Study of Machine Learning Models Using RNA-seq Data.bioRxiv : the preprint server for biology · 2024Article
- Changes in expression of VGF, SPECC1L, HLA-DRA and RANBP3L act with APOE E4 to alter risk for late onset Alzheimer's disease.Scientific reports · 2024Article
- Predicting gene expression state and prioritizing putative enhancers using 5hmC signal.Genome biology · 2024Article
- Machine Learning-Based Prediction of GliomaCancers · 2024Article
- scHybridBERT: integrating gene regulation and cell graph for spatiotemporal dynamics in single-cell clustering.Briefings in bioinformatics · 2024Article
- An interpretable deep learning framework identifies proteomic drivers of Alzheimer's disease.Frontiers in cell and developmental biology · 2024Article
- Investigation of early molecular alterations in tauopathy with generative adversarial networks.Scientific reports · 2023Article
- Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer's disease progression and heterogeneity.Science advances · 2022Article
- Deep learning-based brain transcriptomic signatures associated with the neuropathological and clinical severity of Alzheimer's disease.Brain communications · 2022Article
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Authors and funding
7 authors at 4 institutions in 2 countries.
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Abstract
Deep neural networks (DNNs) capture complex relationships among variables, however, because they require copious samples, their potential has yet to be fully tapped for understanding relationships between gene expression and human phenotypes. Here we introduce an analysis framework, namely MD-AD (Multi-task Deep learning for Alzheimer's Disease neuropathology), which leverages an unexpected synergy between DNNs and multi-cohort settings. In these settings, true joint analysis can be stymied using conventional statistical methods, which require "harmonized" phenotypes and tend to capture cohort-level variations, obscuring subtler true disease signals. Instead, MD-AD incorporates related phenotypes sparsely measured across cohorts, and learns interactions between genes and phenotypes not discovered using linear models, identifying subtler signals than cohort-level variations which can be uniquely recapitulated in animal models and across tissues. We show that MD-AD exploits sex-specific relationships between microglial immune response and neuropathology, providing a nuanced context for the association between inflammatory genes and Alzheimer's Disease.
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