Back to search

Article

The results of Transcriptome-wide Mendelian Randomization (TWMR) in large-scale populations can directly validate, across scales, the results of causal inference from deep learning combined with double machine learning on single-cell transcriptomes of human samples

2026-03-19

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Objective</h4> <h4>Aim: </h4> ing at the core problems prevalent in biomedical research, including the “translational distance”, the difficulty in aligning cross-scale studies, and the lack of direct validation of single-cell systems biology models in human samples, this study aims to verify whether the results of transcriptome-wide Mendelian randomization (TWMR) based on large-scale popula...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
9b4d765d-f84a-5696-af02-4050e591e4f5
DOI
10.64898/2026.03.16.26348532
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
The results of Transcriptome-wide Mendelian Randomization (TWMR) in large-scale populations can directly validate, across scales, the results of causal inference from deep learning combined with double machine learning on single-cell transcriptomes of human samplesDOI 10.64898/2026.03.16.26348532
The results of Transcriptome-wide Mendelian Randomization (TWMR) in large-scale populations can directly validate, across scales, the results of causal inference from deep learning combined with double machine learning on single-cell transcriptomes of human samplesSemantic proximity
The results of Transcriptome-wide Mendelian Randomization (TWMR) in large-scale populations can directly validate, across scales, the results of causal inference from deep learning combined with double machine learning on single-cell transcriptomes of human samplesSemantic proximity
A multi-tissue mendelian randomization method based on eQTL data for mapping tissue-specific disease genes.Semantic proximity
Integrated analysis of single-cell RNA-seq, bulk RNA-seq, Mendelian randomization, and eQTL reveals T cell-related nomogram model and subtype classification in rheumatoid arthritisSemantic proximity
Single-cell transcriptome-wide Mendelian randomization and colocalization analyses uncover cell-specific mechanisms in atherosclerotic cardiovascular diseaseSemantic proximity
Multi-tissue integrated Mendelian randomization method identifies disease risk genes.Semantic proximity
Causal Inference Methods to Integrate Omics and Complex Traits.Semantic proximity
Single-cell transcriptome-wide Mendelian randomization and colocalization reveals immune-mediated regulatory mechanisms and drug targets for COVID-19Semantic proximity
Mendelian randomization while jointly modeling cis genetics identifies causal relationships between gene expression and lipids.Semantic proximity
Single-cell transcriptome-wide Mendelian randomization and colocalization analyses uncover cell-specific mechanisms in atherosclerotic cardiovascular disease.Semantic proximity
Single-Cell Mendelian Randomization Identifies Cell-Type Specific Genetic Drivers of Lung Cancer SubtypesSemantic proximity
Estimating cell compositions and cell-type-specific expressions from GWAS data using invariant causal prediction, deep learning and regularized matrix completion: Bridging GWAS and single-cell resolution in Biobank-scale studiesSemantic proximity
Select a neighboring publication to make it the new centre.