Sensitive functional pathway analysis using Bioinformatics and Graph Networks

Most approaches for the analysis genome-scale data first look for signals in the data and then separately seek a functional interpretation.

We have established an approach that integrates functional pathway knowledge and genome scale data in graph networks, originally on a large range of cancer patient cohorts, demonstrating exceptional sensitivity even for small datasets where other approaches fail (NAR 2023). See https://bipress.boku.ac.at/publications/vilon-a-multi-layer-network-approach-to-data-integration-demonstrated-for-patient-stratification/

We have since also validated it on other types of human data (cardiovascular).

We would now be delighted to demonstrate it on other diverse applications.