Understand your targets and their effects
Reveal target-disease relationships and identify novel druggable targets using our state-of-the-art computational target identification platform and services.
Flexible services to support your team
Data access and processing
Pathway analysis + clustering
Target ranking + nomination
Expand your disease understanding by investigating the underlying biological processes that explain your data.
Reduce thousands of potential targets to a highly curated set, supported by reports and analyses, to find safer, druggable targets.
Kenneth Vielsted Christensen,
Just because you can predict what’s going to happen does not mean you have an explanation for the phenomenon.
The QLattice, a new explainable AI algorithm, can cut through the noise of omics data sets and point to the most relevant inputs and models.
A 17 minute video about Abzu’s origins and an impactful application in life science.
Publications + preprints
Identifying interactions in omics data for clinical biomarker discovery using symbolic regression
Niels Johan Christensen, Samuel Demharter, Meera Machado, Lykke Pedersen, Marco Salvatore, Valdemar Stentoft-Hansen, Miquel Triana Iglesias. Bioinformatics (2022).
Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths
Wilstrup, C., Cave, C. BMC Medical Informatics Decision Making 22, 196 (2022).
Explainable “White-box” machine learning is the way forward in pre-eclampsia screening
Michael Christiansen, MD, FRCPath, Casper Wilstrup, Paula L. Hedley, PhD, MPH. American Journal of Obstetrics and Gynecology, S0002-9378 (2022).
Symbolic regression analysis of interactions between first trimester maternal serum adipokines in pregnancies which develop pre-eclampsia
Casper Wilstrup, Paula L. Hedley, Line Rode, Sophie Placing, Karen R. Wøjdemann, Anne-Cathrine Shalmi, Karin Sundberg, Michael Christiansen