ABZU

Accelerating life science + drug development

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Basic research

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Lead development and optimization

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Preclinical and clinical

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After approval

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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.

What people are saying

Working with Abzu and using the QLattice to analyze our clinical data has provided us with new insights and helped us generate new hypotheses for exploring the potential for a biomarker-based enrichment strategy across cancer.
Art Krieg
Art KriegCSO @ Checkmate Pharmaceuticals
Although Abzu's QLattice (and its collaborative scientists) have helped improve the performance of our drug design algorithms, it's the explainability of the QLattice models that is — in our setting — the most valuable feature for us.
Morten Lindow
Morten LindowMatrix Lead, Therapeutic Modalities @ Roche
Collaborating with Abzu has led to highly significant new knowledge. Using the QLattice in combination with biomarkers as well as medical and social register data is a very promising approach to personalised medicine.
Michael Christiansen
Michael ChristiansenResearch Director @ SSI + Professor @ KU
Hypotheses generated by Abzu's explainable AI QLattice technology are brilliantly put into context with our knowledge graphs. I believe a combined setup like ours is a very realistic picture of the future of data science.
Umut Eser
Umut EserChief Innovation Officer @ Dr.Evidence
While we consider ourselves world-leading in the computational design of oligonucleotide-based therapies, our collaboration with Abzu has added new aspects when applying machine learning to drug discovery data.
Morten Lindow
Morten LindowMatrix Lead, Therapeutic Modalities @ ROCHE
Abzu's QLattice helps determine the precise genomic locations for controlling transcription factor binding and modulating gene expression of a given target. This is a major step forward for genomic medicines.
Thomas Tibbitts
Thomas TibbittsCEO + Founder @ Dogodan Therapeutics
Abzu’s explainable artificial intelligence technology and in-house RNA and data science capabilities, combined with their service-based business model, is a key stepping stone executing our R&D strategy.
Kenneth Vielsted Christensen
Kenneth Vielsted ChristensenCSO @ Contera Pharma
Collaborating with Abzu helped identify quantifiable key drivers that led to discovering the key biomarkers involved with SARS-CoV-2 infection. Abzu was able to enrich our understanding and produce novel analyses.
Afshin Beheshti
Afshin BeheshtiPresident @ COVID-19 International Research Team

Abzu recognized as a Cool Vendor in artificial intelligence

Abzu is named a “Cool Vendor” in the 2022 Gartner® “AI Governance and Responsible AI — From Principles to Practice” report.

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Abzu is a Gartner Cool Vendor 2022

Advancing scientific research

Aalborg University
COV-IRT Covid International Research Team
DTU
Hvidovre Hospital
IBEC
Statens Serum Institut - SSI
University of Copenhagen
University of Oxford
Aalborg University
COV-IRT Covid International Research Team
DTU
Hvidovre Hospital
IBEC

Publications + preprints

BMC Medical Informatics and Decision Making

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).

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Abzu medRxiv preprint

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

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American Journal of Obstetrics and Gynecology

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).

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Abzu published in Bioinformatics

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).

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AppliedEnergy

Explainable long-term building energy consumption prediction using QLattice

Simon Wenninger, Can Kaymakci, Christian Wiethe. Applied Energy 308, 0306-2619 (2022).

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Resarch paper: The QLattice outperfroms other machine learning methods

Symbolic regression outperforms other models for small data sets

Casper Wilstrup, Jaan Kasak

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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.

 

Learn Abzu’s story in 3 minutes

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Media articles covering Abzu's Explainable AI or results in life science
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Reach us during standard CET business hours at +45 31 23 47 64.