Abzu accelerates R&D projects with superior data analytics
Our world-class scientific expertise, bioinformaticians, and explainable AI radically accelerates the development of new therapies.
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Publications + preprints
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).
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
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).
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).
Explainable long-term building energy consumption prediction using QLattice
Simon Wenninger, Can Kaymakci, Christian Wiethe. Applied Energy 308, 0306-2619 (2022).
Symbolic regression outperforms other models for small data sets
Casper Wilstrup, Jaan Kasak
What makes Abzu unique?
Abzu was founded to build a technology that approaches artificial intelligence in a new way – a way that allows us to find the explanations hidden in data, rather than just build black-box prediction models.
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