P551: MIMI Ranker: An AI-powered tool for pathogenic variant prioritization tested on a diverse dataset of 16K clinical exomes and genomes*

Genetics in Medicine Open 2026 Open access

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Authors: Amber Hogart Begtrup, Hákon Guðbjartsson, María J. Guillen Sacoto, Hildur Olafsdottir, Matthew Greenberg, Flavia M. Facio, Vincent D. Ustach, Amber Begtrup
DOI: 10.1016/j.gimo.2026.104042
Cited by: 0

Abstract

Sifting through thousands of rare genomic variants to find clinically relevant variants is a time consuming and analytically challenging step in genomic diagnostic testing. As demand for clinical exome and genome sequencing (ES/GS) grows, there is an increasing need for scalable tools that support consistent and efficient interpretation across large cohorts. Artificial intelligence (AI) approaches offer a promising solution by learning from accumulated clinical data to algorithmically prioritize pathogenic variants for subsequent analysis.

Fields of Study & Subjects

Genomics and Rare Diseases Artificial Intelligence in Healthcare and Education Cancer Genomics and Diagnostics

Publication details

Journal / source
Genetics in Medicine Open
Volume
4
Pages
104042–104042
Publication date
1 January 2026
Language
EN