Method

Biobank-scale genotype similarity search and dynamic patient-matched cohort creation with GenoSiS

    • 1Computer Science Department, University of Colorado, Boulder, Colorado 80309, USA;
    • 2BioFrontiers Institute, University of Colorado, Boulder, Colorado 80303, USA;
    • 3Intel Labs, Hillsboro, Oregon 97124, USA;
    • 4Colorado Center for Personalized Medicine, University of Colorado, Anschutz Medical Campus, Aurora, Colorado 80045, USA;
    • 5Department of Biomedical Informatics, University of Colorado, Anschutz Medical Campus, Aurora, Colorado 80045, USA
    • 6 These authors contributed equally to this work.
Published July 10, 2026. https://doi.org/10.1101/gr.280278.124
Download PDF Cite Article Permissions Share
cover of Genome Research Vol 36 Issue 7
Current Issue:
July 2026, Vol. 36, No. 7
Focus view

Abstract

Many patients do not experience optimal benefits from medical advances because clinical research does not adequately represent them. Although the diversity of biomedical research cohorts is improving, ensuring that individual patients are adequately represented remains challenging. We propose a new approach, GenoSiS, which leverages machine learning–based similarity search to dynamically find patient-matched cohorts across different populations quickly. These cohorts could serve as reference cohorts to improve a range of clinical analyses, including disease risk score calculations and dosage decisions. Although GenoSiS focuses on finding genetic similarity within a biobank, our similarity search architecture can be extended to represent other medically relevant patient characteristics and search other biobanks.

Back to top
Article contents

Announcement(s)