Multimodal AI-powered spatial biomarkers
Context
Multimodal biomarkers are key to precision medicine
By using AI to analyze multimodal patient data, we categorize patients into distinct subtypes based on their biology.
This allows us to identify spatial biomarkers that drive our AI engines for therapeutic target discovery, clinical trial optimization, and diagnostic development.
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Owkin’s solution
Our AI powered
orthogonal approach
Our orthogonal approach combines data from multiple modalities to fully understand the tumor microenvironment to define phenotypic subtypes.
Our biomarker models are built on high-quality patient data, carefully selected and verified by medical experts from our Federated Research Network.
These models have been designed to be interpretable by researchers and medical experts leading to a multiscale understanding of the disease.
What makes Owkin different?
Owkin’s orthogonal approach leverages multimodal patient data
Owkin biomarkers are expertly validated by KOLs in our Federated Research Network and our internal medical team for critical medical context.
Our models discover patterns in the data that are correlated with the predicted labels from scratch, without any help from medical experts. This means that the models’ predictions are exempt from any existing biases.
Our models are interpretable by nature so our medical experts can gain insights from the predictions the model has made and our data scientists can bring continuous improvement to the model's performance.
Our models are multimodal so they capture the full picture of the patient’s disease heterogeneity.
Case studies
TargetMATCH
A new approach to target discovery
A suite of end-to-end tools that uses multimodal patient data as the input, and outputs the top candidate targets and paired patient subgroups that would most benefit from therapeutic intervention on these targets.
AI drug positioning
Matching the right drug and patient for better responses
For a given drug, we identify novel disease indications and subgroups for development, by aggregating causal evidence from prior knowledge and multimodal patient data.