Emedgene is an AI-powered genomic analysis and interpretation platform applying technology to help geneticists manage their growing workload faster and with higher accuracy. This is achieved with an AI engine that has learned to perform genomics research, after training with millions of data points from patient cases, databases, and the most recent genomics publications.

While sequencing is becoming easier, interpreting results is a manual and lengthy research process that forms a bottleneck to the adoption of genetic-based care. Emedgene’s machine learning genomics engine automates interpretation by generating an accurate shortlist of potential causative variants along with their supporting evidence from the literature and databases, significantly reducing the time interpret a genomics case. The algorithms use a proprietary knowledge graph of variants, genes, diseases, phenotypes, and connections, which includes information extracted from literature with Natural Language Processing. Overall, the system automates time-intensive aspects of NGS analysis and research, augmenting lab analysis capabilities. Using emedgene, healthcare providers will be able to scale personalized care to more patients through higher lab throughput and improved yield.

In a study of 180 previously solved whole-exome sequencing cases performed with Baylor Genetics, Emedgene’s platform successfully identified the causative variant (in a shortlist of 10 variants) in 96% of cases.

Emedgene recently announced Pathorolo, a machine learning classifier that identifies which past open cases can be successfully reanalyzed. The model can be used to reevaluate the 50% of genomics cases that are typically not solved initially, and increase the yield on NGS testing.

Emedgene is used by leading US labs including Baylor Genetics, Baylor College of Medicine, Greenwood Genetic Center, TGEN and others.

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