ArticlePLoS computational biology2022
Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- Winner's Curse Free Robust Mendelian Randomization with Summary Data.Journal of the American Statistical Association · 2026Article
- Uncertainty-aware genomic deep learning with knowledge distillation.NPJ artificial intelligence · 2026Article
- Distilling the Knowledge of Ensembles for Uncertainty-Aware Genomic Deep Learning.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Demystifying inconsistent two-sample mendelian randomization estimations using selection diagram.BMC medical research methodology · 2025Article
- Twenty years of genome-wide association studies: Health translation challenges and AI opportunities.European journal of human genetics : EJHG · 2025Review
- Can classical statistics and deep learning converge on explainable, causally driven target discovery?DNA research : an international journal for rapid publication of reports on genes and genomes · 2025Review
- Exploring novel biomarkers and immunotherapeutic targets for biofeedback therapies to reveal the tumor-associated immune microenvironment through a multimetric analysis of kidney renal clear cell carcinoma.Discover oncology · 2025Article
- Uncertainty-aware genomic deep learning with knowledge distillation.bioRxiv : the preprint server for biology · 2024Article
- Mendelian Randomization Studies in Atherosclerotic Cardiovascular Diseases.Journal of lipid and atherosclerosis · 2024Review
- An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs.Science China. Life sciences · 2024Review
- Artificial intelligence for dementia genetics and omics.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multi-task deep learning (DL) models can accurately predict diverse genomic marks from sequence, but whether these models learn the causal relationships between genomic marks is unknown. Here, we describe Deep Mendelian Randomization (DeepMR), a method for estimating causal relationships between genomic marks learned by genomic DL models. By combining Mendelian randomization with in silico mutagenesis, DeepMR obtains local (locus specific) and global estimates of (an assumed) linear causal relationship between marks. In a simulation designed to test recovery of pairwise causal relations between transcription factors (TFs), DeepMR gives accurate and unbiased estimates of the 'true' global causal effect, but its coverage decays in the presence of sequence-dependent confounding. We then apply DeepMR to examine the global relationships learned by a state-of-the-art DL model, BPNet, between TFs involved in reprogramming. DeepMR's causal effect estimates validate previously hypothesized relationships between TFs and suggest new relationships for future investigation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.