Preprint: 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.

WHY IT MATTERS

If validated, this approach could accelerate how researchers identify disease-causing genes in rheumatologic conditions, potentially leading to faster development of targeted treatments for patients with autoimmune and inflammatory diseases.

Scientists are testing a new way to understand how genes cause diseases by combining two different research methods: one that studies genes in large groups of people, and another that looks at individual cells in the lab. This study checks whether both methods give the same answers, which would help researchers trust their findings more and move treatments from the lab to real patients faster.

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. Authors: ye, w. et al. Server: medRxiv Category: rheumatology Abstract: ObjectiveAiming 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 populations are consistent with the causal inference results of deep learning combined with double machine learning (DML) using single-cell transcriptome data from human samples, to clarify whether statistical biology and systems b