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.
We transform your raw data to an anaylsis-ready format using our extensive bioinformatics and data science toolkits.
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.
We convert our models into clinical decision support systems to help clinicians make interpretable, data-driven decisions.
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.
Forms are completed faster because our proprietary AI utilizes minimum complexity for best model performance.
Tailored and user-friendly user experience with your custom-designed clinical decision support system.
Awards + nominations
We are thrilled and honored to be recognized for our innovative technology and unique organization.
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 al. Nature Communication, 11 (2020).
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