Our variant interpretation platform enables genetic testing labs to give more people with genetic diseases a clear and fast diagnosis.
Over 300 million people around the world live with a rare genetic disease. For most, the journey from first symptom to diagnosis lasts over 5 years, with many receiving incorrect or no diagnosis at all. Without diagnosis, patients remain without access to targeted medical interventions and potentially life-saving treatments.
New sequencing technologies are allowing millions of people to benefit from diagnostic genetic testing. However, the last step in genetic testing – variant interpretation – remains laborious and costly. A lab can spend hours, days or even weeks interpreting data from a single patient. Additionally, because of limited understanding of the consequences of variants throughout our genome, genetic test results are often inconclusive. Only about 30% of patients undergoing next-generation sequencing receive a diagnosis, leaving the majority without a clear answer.
A NEW APPROACH
Our ML model classifies and prioritizes variants in a way that is auditable and reproducible. Moreover, it learns from new data, but also benefits from expert knowledge: in addition to 30+ data sources, we modeled a decade of experience in clinical genetics.
To solve more variants of unknown significance, the data we currently have is not enough. We solve this by generating new data in-house in a scalable manner with massive parallel experiments leveraging new approaches in synthetic biology.
WHO WE ARE
David is an entrepreneur who worked on digital health applications at a ETH Zurich lab and gained experience with ML in healthcare at Merantix AG. He experienced the challenge of genetic disease diagnosis in his private life, initiated two technology organizations and holds a business degree from the University of St. Gallen.
Rocío is a medical doctor with a PhD in human genetics and a decade of experience analyzing genetic data and studying variants. She completed her PhD at Radboud University Nijmegen and was a postdoctoral fellow at the Max-Planck Institute for Molecular Genetics in Berlin, with over 15 publications in prestigious journals.
David is a computational neuroscientist specialized in machine learning, with vast experience applying ML to biological and clinical data. He received his PhD from the University of Amsterdam and carried out senior postdoctoral research at the Donders Institute for Brain, Cognition and Behaviour.
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