DMDPipe - Disease Module Discovery Pipeline
Title: DMDPipe - Disease Module Discovery Pipeline
SNIC Project: SNIC 2020/5-177
Project Type: SNIC Medium Compute
Principal Investigator: Zelmina Lubovac <>
Affiliation: Högskolan i Skövde
Duration: 2020-04-01 – 2021-04-01
Classification: 10203


Network-based approaches have been shown to be powerful tools for analyzing complex diseases. One of the commonly used strategies is to identify disease-related network modules. It is widely accepted that disease-related genes are not randomly distributed when mapped on the human protein-protein interaction (PPI) network. They form modules that are co-localized in the PPI network and overlap across “omics”. Network-based approaches have been used for asthma and multiple sclerosis (MS) prognosis for identifying new combinations of biomarkers. However, a key limitation of current approaches is that they do not incorporate individualized variations, which is necessary when aiming to develop methods for individualized diagnostics. In this project, we will model multi-layered modules by integrating omics for a subset of patients and using subgroup specific networks, to identify patient-specific regulatory transcription factors (TFs). We aim to develop mathematical models to predict how the upstream regulators could be manipulated, and build an analysis pipeline for asthma and MS that can be used to suggest new individualized therapeutic interventions. The expected contributions of this project will be of use for drug developers as it is becoming increasingly apparent that the large variety between patients with the same disease stipulates therapeutic subdivision of patients, i.e. individualized diagnostics and treatments based on stratification of diseases. The expected results of the project also imply the potential design of diagnostic multimarker panels that will speed up diagnosis and increase the precision for asthma and MS, thereby supporting the clinical needs of precision medicine.