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GenoSiS: A Biobank-Scale Genotype Similarity Search Architecture for Creating Dynamic Patient-Match Cohorts.

Kristen Schneider^1,2, Murad Chowdhury², Mariano Tepper³

¹Computer Science Department, University of Colorado, Boulder, CO, USA.

Biorxiv : the Preprint Server for Biology

|November 18, 2024

Summary

This summary is machine-generated.

Clinical research often fails to represent diverse patient populations. A new machine learning approach, GenoSiS, rapidly identifies genetically similar patient cohorts for improved clinical analyses and personalized medicine.

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Area of Science:

Biomedical research
Genomics
Clinical informatics

Background:

Biomedical research cohorts often lack adequate patient representation, limiting the benefits of medical advances.
Ensuring individual patient representation in clinical research remains a significant challenge despite improvements in cohort diversity.

Purpose of the Study:

To introduce GenoSiS, a novel machine learning-based approach for dynamic patient-matched cohort identification.
To enable the creation of reference cohorts for enhancing clinical analyses.

Main Methods:

Leveraging machine learning-based similarity search to identify patient-matched cohorts across diverse populations.
Focusing on genetic similarity within a biobank as a primary application.

Main Results:

GenoSiS enables rapid identification of relevant patient cohorts.
The approach facilitates the creation of reference cohorts for improved clinical decision-making.

Conclusions:

GenoSiS offers a scalable solution for improving patient representation in clinical research.
The similarity search architecture can be extended to incorporate other patient characteristics and biobanks for broader applications.