Create a high-performing clinical decision support system powered by explainable AI

Create a clinical decision support system

Get closer to real disease understanding with Abzu’s researchers, software engineers, and data scientists.

Data-driven decision making in the clinic, faster

Our proprietary, explainable AI, combined with our expertise in bioinformatics, data science, and software engineering, take you from early research to application.

Flexibility to suit your needs

Create a powerful and user-friendly clinical decision support system.


We help clinical researchers with data transformation and explainable predictions, and develop data-driven decision making tools for the clinic.

Data processing

We transform your raw data to an anaylsis-ready format using our extensive bioinformatics and data science toolkits.

Predictive analytics

Our proprietary AI develops high-performance and explainable models, revealing the hidden mechanisms in your data.

Medical writing + dissemination

We put our models into scientific context with reviewed literature, and help you disseminate results and write publications.

Application development

We convert our models into clinical decision support systems to help clinicians make interpretable, data-driven decisions.

Abzu accelerates scientific research

Publications + preprints

Why Abzu?

Work with a highly-skilled, multidisciplinary team of researchers, software engineers, and data scientists, ready to collaborate with you from early research to a finished app.

Compliance with increasingly complex regulatory demand is easy with our explainable models.

Completing forms is easy

Forms are completed faster because our proprietary AI utilizes minimum complexity for best model performance.

Cellphone with all the likes

Tailored and user-friendly user experience with your custom-designed clinical decision support system.


Abzu and Abzoids are published in:

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).

Tracing the origin of adult intestinal stem cells

Guiu, J., Hannezo, E., Yui, S. Demharter, S., et al. Nature, 570 (2019).

Identification of epilepsy-associated neuronal subtypes and gene expression underlying epileptogenesis

Pfisterer, U., Petukhov, V., Demharter, S. et alNature Communication, 11 (2020).

Contact Abzu

We’re just a bunch of nice nerds building something new and awesome. How can we help you?

Fill out the form below, reach out to our sales at, or write to a scientist at


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