Evidence is mounting that genome sequencing should be performed as a first-tier test for children suspected of suffering from a genetic disorder in multiple pediatric settings, especially for those in the intensive care unit. As more children are sequenced, the pressures to increase the diagnostic rate and the time to result, while reducing the cost of the process, are amplified.
Interpretation of whole-genome or exome variants to diagnose rare genetic diseases continues to be a major bottleneck, as it often consists of time-consuming iterative variant filtering coupled with evidence review for large lists of candidate variants.
In this webinar, Dr. De La Vega will introduce the Fabric GEM AI algorithm and demonstrate how it significantly simplifies and improves disease-causing variant identification over prior methods, substantially reducing genome interpretation time in the diagnosis of monogenic disease, and could allow cost-effective, automated reanalysis of undiagnosed cases over time.Â
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Francisco De La Vega, D. Sc., is the Chief Scientific Officer and Senior Vice Present of Research and Development at Fabric Genomics. Francisco is a geneticist and computational biologist with interests in clinical and population genomics. Francisco is Adjunct Professor in the Department of Biomedical Data Science of the Stanford School of Medicine, a Director of the International Society of Computational Biology, and has been a member of the NIST Genome-in-a-Bottle Consortium, the PanCancer Analysis of Whole Genomes project of the ICGC, and the Steering Committee of the 1000 Genomes Project. In 2009 he was inducted to the Innovation & Invention Society of Life Technologies, a program that recognized the company’s most elite inventors, and in 2008 was a co-recipient of the Bio-IT World Best Practices Award in Basic Research. Francisco received his Doctor of Science degree in Genetics and Molecular Biology from CINVESTAV, the leading research and post-graduate education institution of Mexico.
