Matthijs Moed

Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data

The paper develops interpretable neural networks for multi-omics data to predict smoking status, age, and LDL levels, showing improved performance and generalizability across cohorts.

Genome-wide identification of directed gene networks using large-scale population genomics data

The paper identifies 49 gene drivers of downstream transcriptional changes using population genomics data and RNA-sequencing from 3,072 individuals.

Disease variants alter transcription factor levels and methylation of their binding sites

The paper identifies trans-meQTLs linking disease variants to methylation changes at transcription factor binding sites, revealing eight-fold more downstream effects than trans-eQTLs.

Hypothesis-free identification of modulators of genetic risk factors

The paper identifies context-dependent eQTLs in peripheral blood RNA-seq data from Dutch individuals using a hypothesis-free approach, revealing gene expression influenced by cell type and signaling pathways.