case study

Improving cancer biomarker discovery and design

June 13, 2022

Our simple models accurately predicted and explained treatment responses for Checkmate Pharmaceuticals

We discovered and designed composite biomarkers for more successful clinical trials

We explored genes and gene-signature interactions to identify and explain treatment responses with Checkmate Pharmaceuticals

There is a bottleneck in biomarker discovery

Collecting patient data from clinical trials is time-consuming and expensive.

Scientists and researchers are often left with small and disjointed datasets (few samples and disparate pieces of information) to explain drug responses. The result is that the FDA approves only 1 to 3 biomarkers for clinical use each year^[1].

The customer: Checkmate Pharmaceuticals

We supported a single Computational Biologist with expertise in target discovery and target validation in oncology.

Why Abzu?

Because traditional machine learning techniques don’t deliver enough actionable insights for scientists and researchers.

Technology + services

Abzu’s flexible services include our explainable AI, the QLattice®, and data access, data processing, and regulatory compliance and approval.

PRODUCTS + SERVICES >

TESTIMONIALS >

WHAT IS A QLATTICE? >

The Abzoids

The Abzoids dedicated to this project were Lykke Pedersen, PhD in Biophysics, Valdemar Stentoft-Hansen, MSc in Economics, and Martin Mathiasen, Business + Customers.

MEET THE ABZOIDS >

WHY ABZU? >

Simple models more accurately predict and explain treatment responses

Flexibility to suit your needs

Data access + data processing

We enhance your research with biobank access and prepare your raw data for analysis.

High-performing + interpretable results

Receive a set of composite biomarker signatures with clear and explainable model interpretations.

Regularly compliance + approval

Confidently nominate your signature with our comprehensive report and support.

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.

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.

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.

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.

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