We are
Scientists
Clinicians
Engineers
We are
Scientists
Clinicians
Engineers


Built on decades of experience in epidemiology, genetics and machine learning, we are committed to making drugs available for patients everywhere.

Alireza Sohofi
Founding Engineer
Lena Kaisinger, PhD
Genetic Epidemiologist
Jakob Steinfeldt, MD
Co-Founder and CSO
Thore Buergel, PhD
Co-Founder and CEO
Our Science
A predictive atlas of disease onset from retinal fundus photographs
We have unlock the predictive potential of retinal images for genetic discovery and disease risk assessment across more than 750 conditions. Our approach not only outperforms traditional methods in predicting diseases but also uncovers new genetic associations. We believe this fusion of AI and genetics will transform target identification and prioritization.
A scalable, secure, and interoperable platform for deep data-driven health management
The surge in biomedical data from wearable sensors, electronic health records, and genomics is revolutionizing healthcare, offering significant health improvement opportunities while posing challenges in data management and analysis. To meet these challenges, we developed a tool utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics.
Large-scale exome sequence analysis identifies sex- and age-specific determinants of obesity
Obesity contributes substantially to the global burden of disease. We studied how genetics influence obesity using UK Biobank data and found certain rare gene variations impact obesity differently in men, women, and children showing how these variations affect processes like cell death and DNA damage.
Understanding the genetic complexity of puberty timing across the allele frequency spectrum
The timing of puberty affects future health outcomes. We investigated the underlying biological mechanisms, performing multi-ancestry genetic analyses in ∼800,000 women, identifying 1,080 independent signals associated with puberty timing.
Medical history predicts phenome-wide disease onset (in press)
Current medicine lacks data-driven guidance, underutilizing the predictive value of medical histories for diseases. We explored the potential of the medical history to inform on the phenome-wide risk of onset for 1,883 disease endpoints across clinical specialties.
Metabolomic Data Predicts Common Disease Onset
Risk stratification is critical for the identification of high-risk individuals. Here we show the potential of NMR metabolomic profiles to predict multidisease onset across 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal and neurological diseases and cancers.
Join us, we are actively hiring!


Come help us scale, explore and build the infrastructure that brings better drugs to patients. If you are sure you can help, reach out. We look forward to hearing from you.