Tom Michoel

Lemonite integrates transcriptomics and metabolomics data to identify regulatory metabolites acting on gene modules in glioblastoma and inflammatory bowel disease.

The paper introduces FlowGAT, a hybrid FBA-machine learning method to predict gene essentiality in E. coli by leveraging metabolic network structure and knock-out fitness data.

The paper identifies genetic variants associated with plasma cortisol and cardiovascular disease, revealing gene networks in liver and adipose tissues regulated by corticosteroid binding globulin and the glucocorticoid receptor.

This paper introduces a restricted maximum-likelihood method to estimate latent variance components in gene expression data, improving efficiency and accuracy over gradient-based approaches.

The paper investigates gene expression changes during spontaneous migraine attacks and identifies differentially expressed genes and pathways associated with migraine pathophysiology.

This study compares instrumental variable and mediation-based methods for causal gene network reconstruction in yeast using a segregating population dataset and known interactions.

The paper introduces a method for inferring high-dimensional Bayesian gene networks using genetic node ordering, showing improved efficiency and accuracy over conventional methods.

This study uses long-read sequencing to analyze VSG diversity in T. brucei infections, revealing higher expressed diversity and early mosaic gene formation than previously observed.

The paper uses Findr to infer causal gene networks from transcriptomic data, identifying major hub genes in the Geuvadis dataset.

The paper infers causal gene regulatory networks using enhancer RNA expression as a causal anchor and validates predictions against experimental data.

The paper introduces lassopv, a method to control false discoveries in Bayesian gene networks using p-values from lasso regression, achieving optimal false discovery control compared to existing methods.

The paper introduces Findr, a method for causal inference between gene expression traits using genome-transcriptome data, outperforming existing methods in accuracy and speed.

The paper uses transcriptomic data from mouse and rat liver and heart to identify functionally equivalent orthologs using co-expression network analysis.