Article

Robust discovery of causal gene networks via measurement error estimation and correction

2023-05-10

Abstract excerpt

Discovering causal relations among genes from observational data is a fundamental problem in systems biology, especially in humans where direct gene interventions or perturbations are unethical/infeasible. Furthermore, causality is emerging as an integral factor for building interpretable and generalizable machine-learning models of complex phenotypes. Existing methods can discover causal relations from observed g...

Topics

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

Identifiers and source

Literature Corpus work: 0b590912-a044-5bfc-80a9-365ca39bcbff
DOI: 10.1101/2023.05.09.540002

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Robust discovery of causal gene networks via measurement error estimation and correctionDOI 10.1101/2023.05.09.540002Read

NLCD: A method to discover nonlinear causal relations among genesSemantic proximity Read

MR-GGI: accurate inference of gene-gene interactions using Mendelian randomization.Semantic proximity Read

Efficient and accurate causal inference with hidden con-founders from genome-transcriptome variation dataSemantic proximity Read

SIGNET: Transcriptome-wide Causal Inference for Gene Regulatory NetworksSemantic proximity Read

Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits.Semantic proximity Read

Causal modeling of gene effects from regulators to programs to traits: integration of genetic associations and Perturb-seqSemantic proximity Read

Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast.Semantic proximity Read

Causal inference for multiple risk factors and diseases from genomics dataSemantic proximity Read

Orienting the causal relationship between imprecisely measured traits using GWAS summary data.Semantic proximity Read

Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data.Semantic proximity Read

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 Read

DCI: Learning Causal Differences between Gene Regulatory NetworksSemantic proximity Read