In this webinar, an expert panel discusses how they used a genomic search engine to mine the genomic literature for two key applications: variant interpretation and the development of evidence-based diagnostic gene panels.

Nikoletta Sidiropoulos and David Seward from the University of Vermont College of Medicine first discuss their approach and the tools used to quickly and thoroughly mine the scientific literature to interpret variants in somatic cancer cases.

Next, Victor Weigman from Q2 Solutions presents an evidence-based method that his team used to select the content for gene panels by mining millions of full-text genomic articles to identify disease-gene-variant relationships. Dr. Weigman discusses how he created an evidence-based gene panel in under a week with prioritized literature citations for each biomarker.

Finally, Mark Kiel, founder and chief scientific officer of Genomenon, discusses a comprehensive, evidence-based cancer panel that was produced using automated machine learning techniques. The pan-hematopoetic cancer panel is a comprehensive cancer panel of more than 300 genes supported by specific literature citations from among millions of research publications. Dr. Kiel discusses how his team used the Mastermind Genomic Search Engine software to objectively correlate genes and genetic variants with the quality and frequency of scientific literature citations.