Olaf Wolkenhauer A

The paper constructs a molecular interaction map of colorectal cancer, identifies disease signatures, and predicts therapeutic targets through simulation and validation.

The paper identifies Clindamycin as a potential inhibitor of pyroptosis in tumor-associated macrophages by integrating network biology and pharmacophore modeling.

The paper presents a computational framework for analyzing SARS-CoV-2-host interactions to identify drug targets and model disease mechanisms.

The paper constructs a knowledge base of molecular interactions linking gastrointestinal diseases, malnutrition, and sarcopenia and provides an interactive tool for in silico analysis.

The paper develops a MINERVA plugin suite for integrating multi-omics data and performing in silico perturbation experiments on disease maps using RNA-seq datasets from the Atlas of Inflammation Resolution.

The paper introduces a synthetic oversampling method to annotate rare cell types in single-cell RNA-sequencing data, demonstrating high accuracy and low false positives in two use cases.

AMES uses deep learning to evaluate sarcomere structures in cardiomyocytes, achieving high accuracy in predicting sarcomerisation and cell source.

This study uses deep learning and label-free imaging flow cytometry to classify red blood cell morphologies during storage, achieving 90% accuracy and predicting expiration dates based on morphological changes.

GEMtractor is a tool to extract subnetworks from genome-scale metabolic models encoded in SBML, allowing focused analysis on reaction- and enzyme-centric views.

The paper describes a method using pathway maps to analyze omics data, focusing on cellular senescence and its role in disease.

The paper systematically evaluates centrality measures in yeast protein-protein interaction networks to identify which measures best predict influential proteins.