Klaudia Adamowicz1*, Andreas Maier1, Jan Baumbach1,2 and David B. Blumenthal3
1Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
2Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
3Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universitaet Erlangen-Nuernberg (FAU), Erlangen, Germany
klaudia.adamowicz [at] uni-hamburg.de
Abstract
Given the constraints faced in the development of new drugs, the importance of drug repurposing has reached unprecedented levels. A key aspect of effective drug repurposing lies in the discovery of disease mechanisms and the identification of clusters of diseases with shared mechanistic characteristics. While various methods exist for computing candidate disease mechanisms and clusters, the absence of ground truth presents challenges in validating these predictions through in silico means. This obstacle significantly impedes the widespread adoption of in silico prediction tools, as experimentalists often hesitate to conduct wet-lab validations without clearly quantified initial plausibility.
To address this issue, we introduce DIGEST (in silico validation of disease and gene sets, clusterings or subnetworks). DIGEST is a Python-based validation tool that offers multiple avenues for utilization. It is accessible as a web interface through https://digest-validation.net, as a stand-alone package, or via a REST API. DIGEST streamlines the process of in silico validation by providing fully automated pipelines. These pipelines encompass critical components such as disease and gene ID mapping, enrichment analysis, comparisons of shared genes and variants, and background distribution estimation. Additionally, DIGEST incorporates functionality to automatically update the external databases utilized by the pipelines. By employing DIGEST, users gain the ability to assess the statistical significance of candidate mechanisms in terms of functional and genetic coherence. The tool enables the computation of empirical P-values with ease, requiring only a few simple clicks. With its comprehensive and user-friendly features, DIGEST greatly facilitates the evaluation of candidate mechanisms, empowering researchers to quantify the plausibility of predicted mechanisms in a robust and efficient manner.
Keywords: Systems medicine, in silico validation, Functional and genetic coherence
Acknowledgement: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 777111 (A.M., J.B.). This publication reflects only the authors’ view and the European Commission is not responsible for any use that may be made of the information it contains. This work was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of the e:Med research and funding concept (grant 01ZX1908A and grant 01ZX1910D) (J.B.). J.B. was partially funded by his VILLUM Young Investigator Grant No. 13154.
