We combine cutting-edge machine learning and biology to identify novel biomarkers.
Biomarkers
AI-powered biomarkers
We apply AI to multimodal, KOL-defined data to subtype patients and identify novel biomarkers to inform drug discovery, de-risk clinical trials and develop and deploy diagnostics in clinical practice.
Multimodal
to capture the full complex picture of the disease.
Interpretable
to extract key biological insights from heat maps.
Clinically validated
to ensure clinical utility by leaders in the field.
Multimodal
to capture the full complex picture of the disease.
Interpretable
to extract key biological insights from heat maps.
Clinically validated
to ensure clinical utility by leaders in the field.
Multimodal
to capture the full complex picture of the disease.
Interpretable
to extract key biological insights from heat maps.
Clinically validated
to ensure clinical utility by leaders in the field.
Multimodal
to capture the full complex picture of the disease.
Interpretable
to extract key biological insights from heat maps.
Clinically validated
to ensure clinical utility by leaders in the field.
Our AI-powered orthogonal approach
We apply interpretable AI to multimodal patient data and pioneer novel technologies such as histogenomics and spatial omics to discover AI-powered biomarkers.
Contact usStratification (predictive) biomarkers
These biomarkers predict the best treatment for each patient by analyzing subgroups of good and bad responders. → To better predict the response to treatment, create surrogate markers and to steer research for future targets.
Find out moreRisk-score (prognostic) biomarkers
These biomarkers predict the course of the disease in a patient (outcome, overall survival, metastatic relapse). → To inform therapeutic decisions, accelerate clinical trial enrolment and design more accurate translational and early-stage trials.
Find out moreScreening (diagnostic) biomarkers
These biomarkers help us identify patients with a particular disease characteristic. → To define and accelerate clinical trial enrolment.
Find out moreCase study
Impact
Impact
Biopharma
High-value subgroups
BioPharma companies can use this model to select high-value subgroups of patients that are most likely to respond to the ICI being tested. This improves the statistical power of the trial.
Trial optimization
This also results in better selection of trial participants, success rates across trial phases and ultimately regulatory approval and more precise marketing.
Take a closer look
