Over 300 million people globally live with a rare genetic disease and for many, the journey from the first symptoms to a correct diagnosis can take more than 5 years. Now, new technology is allowing millions to benefit from genetic testing, while targeted therapies for previously untreatable disorders are being developed.
Yet the last step in genetic testing – variant interpretation – remains laborious and costly. Analyzing data from a single patient can take weeks and often ends in an inconclusive test result. Currently, over 70% of patients do not receive a clear diagnosis after undergoing whole exome or whole genome sequencing.
Advanced machine learning
Our software annotates and classifies variants according to ACMG guidelines. Then, our algorithm prioritizes them based on phenotype, displaying a confidence score for each result.
Adding to over 30 continuously updated public data sources, the predictions of our machine learning model rely on unique biological and computational data that we generate in-house and accumulate.
Expert genomics knowledge
Our approach leverages over a decade of experience in analyzing genomic data and studying the effects of genetic variation using biological and computational techniques.
Benefits for Medical Labs
Save time & money
The automated process means that you get a result within minutes, and the added transparency from our confidence scores makes our predictions much more reliable.
Increase your diagnostic yield
By relying on both computational as well as biological methods to variant interpretation, our software will help you increase your diagnostic yield.
Keep your flexibility
We are completely focused on automating variant interpretation and allow you to integrate our software with any existing pipeline, both in-house or third-party.
Co-Founder & CEO
David is an entrepreneur with experience from working with 4 leading health care organizations and a strong personal motivation to empower more patients with a clear diagnosis. He worked on digital health applications at a ETH Zurich lab, applied AI to medical imaging at one of Europe's leading AI companies, and provided medical care to hospital patients as a Medic in the Swiss Army. He initiated two technology organizations and holds a degree in business from the University of St. Gallen.
Rocío Acuña-Hidalgo, MD, PhD
Co-Founder & CTO
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. She has numerous publications in prestigious journals and received the Young Investigator Award for Outstanding Science from the European Society for Human Genetics for her doctoral research (2016).
David Neville, PhD
Head of Machine Learning
David is a computational neuroscientist specialized in artificial intelligence, with vast experience working in interdisciplinary teams. He received his PhD from the University of Amsterdam and carried out postdoctoral research at the Donders Institute for Cognitive Neuroscience. He has extensive experience applying machine learning to biological and clinical data to gain insights into human biological processes relevant to health and disease.
Scientific, Clinical & Business Advisors
Lea Starita, PhD
Assistant Professor at Department of Genome Sciences at the University of Washington, Co-Director of Brotman Baty Advanced Technology Lab
Martin Kircher, PhD
Group Leader in Computational Genome Biology, Berlin Institute of Health, Charité Berlin
Han Brunner, MD, PhD
Head of Human Genetics, Radboud University Medical Center & Head of Clinical Genetics, Maastricht University Medical Center
Elgar Fleisch, PhD
Professor of Information Management at ETH Zurich, Professor of Technology Management at University of St. Gallen