Johannes A. Birgmeier

InpherNet improves disease gene hypothesis generation by leveraging gene network neighbors, achieving higher accuracy than existing methods in prioritizing causative genes.

AMELIE 3 automatically reanalyzes undiagnosed Mendelian disease patients using new literature, achieving 80% diagnostic yield with 0.5-1 alerts per patient per year.

The paper identifies transcriptional networks in developing mouse projection neurons by correlating chromatin accessibility with gene expression to understand cell fate diversity.

AMELIE 2 improves Mendelian disorder diagnosis by matching patient phenotypes and genotypes to primary literature, achieving high accuracy and efficiency.

AVADA automatically curates a database of pathogenic variants from full-text literature, outperforming existing methods and providing more variants than ClinVar.

This paper develops secure protocols to protect patient privacy during disease diagnosis by enabling secure computations on both genotypic and phenotypic data in two scenarios: cohort discovery and gene prioritization.

ClinPhen automatically extracts and prioritizes patient phenotypes from medical records to improve genetic disease diagnosis efficiency.

Phrank improves Mendelian disease prioritization by ranking candidate genes or diseases based on phenotype similarity, outperforming existing methods in real patient exome data.

This paper develops secure multiparty computation methods to identify causal variants in Mendelian diseases without exposing patient genomes.

AMELIE automates Mendelian disorder diagnosis by analyzing literature and prioritizing candidate genes, achieving high diagnostic accuracy for singleton patients.