Vanessa Vermeirssen

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

The paper constructs a multi-omics biomolecular network in AML using proteins, metabolites, and histone PTMs to reveal functional entanglement between biomolecules.

This study benchmarks five deep learning models for predicting Hi-C contact maps, finding Epiphany performs best in accuracy, generalization, and loop detection.

The paper compares three computational methods to infer metabolism from single-cell transcriptomics data in glioblastoma, finding tumor-associated macrophages as metabolically dominant.

The paper shows that radiation-induced DNA damage in human forebrain organoids leads to microcephaly through a human-specific p53-E2F4 repression of microcephaly genes.

This study evaluates single-sample network inference methods for cancer subtyping using bulk RNA-seq data from cancer cell lines.

HTSplotter automates data processing, analysis, and visualization for in vitro perturbation screens, supporting multiple experimental types and time-course data.

SUBATOMIC clusters multi-omics networks into interpretable modules, identifies hypoxia-related modules in cancer, and provides functional insights for unannotated genes.