AI-driven deciphering of genetic diseases

Our variant interpretation platform enables genetic testing labs to give more people with genetic diseases a clear and fast diagnosis.

TESTING FOR GENETIC DISEASES

Only about 30% of patients
receive a clear diagnosis.

Wright CF et cols. Nature Reviews Genetics (2018).
Posey JE et cols. Genetics in Medicine (2016).

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 platform leverages two
key innovations to solve this.

Transparent
‍machine learning

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.

High-throughput
synthetic biology

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.

NEW
Early Access

Variant Interpretation Platform

Our platform excels at variant interpretation thanks to our new technology and focus on what truly matters for you: accuracy and speed.
Automated variant classification (proprietary ML & ACMG guidelines)
The core technology enables a significant increase in diagnostic yield
Prioritizes variants according to HPO terms and suggests disease
Ensures full transparency and deep explanations for each result
Supports SNVs and indels in NGS panels, exomes, and (soon) genomes
Secure, encrypted and GDPR-compliant cloud platform with API

We are running an early access
program with selected labs.

By requesting access, I agree that Nostos Genomics can contact me electronically in case of follow-up questions .
Your request was successful.
Looks like we're having trouble

WHO WE ARE

Our Team

David Gorgan

Co-Founder & CEO

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 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, with over 15 publications in prestigious journals.

David Neville, PhD

Head of machine learning

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.

Our team is growing. Would you like to join us? Please reach out here.

MEET OUR EXPERTS

Scientific Advisors

Han Brunner, MD, PhD

Prof. of human genetics, Radboud medical center & head of clinical genetics, maastricht university medical center

Lea Starita, PhD

Assistant prof. at Department of Genome sciences, university of washington

Martin Kircher, PhD

Group leader in computational genome biology, berlin institute of health, charité berlin

Elgar Fleisch, PhD

Prof. of Information Management, ETH ZURICH & PROF. OF TECHNOLOGY MANAGEMENT, university of st. gallen

We are backed by

Entrepreneur First
Venture capital
BMWi
federal grant
AWS Activate
Cloud Resources