developed a random walk approach to identify the regulatory networks underlying common disease from DNA-methylation data (Hawe Nature Genetics 2022) - code

• extended this approach using Bayesian graphical models to make full use of prior networks and the correlation structure in the data (Hawe Genome Medicine 2022) - code

• integrated proteomics and gene expression to identify cis and trans acting eQTL/pQTL in the human atrial appendage (Assum Nature Communications 2022) - code
  • developed a data integration approach that makes use of polygenic risk scores and pathway annotations to identify trans-acting QTL from protein and transcript expression data. We applied it to the atrial fibrillation cohort of the symAtrial consortium to identify candidate core genes of atrial fibrillation (Assum Nature Communications 2022) - code

• reviewed current methods for network reconstruction from multi-omics data (Hawe Frontiers in Genetics 2019)

• developed Speos - a graph neural network approach to predict core genes of complex disease from network, GWAS and gene expression data (Ratajzcak bioRxiv 2023) - code - docs

Simon

Sergey