Fabric Genomics at the 2019
AMP Meeting
November 7-9, 2019
Baltimore Convention Center
Baltimore, Maryland

Booth #2224
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Fabric Genomics is the pioneer of AI-driven genomic interpretation. At AMP, we will demonstrate how panel testing with Fabric ACE enables clinical labs to perform rapid genomic interpretation to uncover critical insights that reduce costs and save lives.


Corporate Workshop: Applying Artificial Intelligence to Accelerate Variant Classification and Clinical Reporting of NGS Gene Panels.

Franciso De La Vega, D.Sc.
Chief Scientific Officer, Fabric Genomics

Corporate Workshop
Wednesday, November 6, 2019
Baltimore Convention Center Room: 345-346

Dr. De La Vega will present data from our new inference engine that leverages deep gene and variant annotation for highly accurate ACMG variant classification for gene panels: Fabric ACE. As a demonstration of the efficiency achieved with ACE in routine testing, Dr. De La Vega will present results of the analysis of a 52-gene inherited cancer panel from a cohort of 2,642 cancer patients of diverse histology, showing a reduction of manual classification greater than 90%. ACE is embedded into Fabric Enterprise for complete FASTQ-to-clinical report workflow and allows labs to accelerate accurate variant interpretation, classification, and clinical reporting down to minutes per case, enabling labs to score variants rapidly, reproducibly, cost-effectively and at scale.

Poster: High-Throughput Genetic Variant Classification for Inherited Cancer Gene Panels Through An AI Inference Engine

Franciso De La Vega, D.Sc.
Chief Scientific Officer, Fabric Genomics

Sahar Nohzadeh-Malakshah, Ph.D.
Senior Bioinformatics Scientist, Fabric Genomics

Poster Session
Friday, November 8, 2019
Baltimore Convention Center Exhibit Hall

A growing number of labs are implementing cancer risk screening tests that sequence panels of cancer genes ranging from a few to over a hundred genes. This growth is driven by both the reduction of sequencing costs and the availability of reimbursement pathways for such tests. However, an important part of the cost involves the assessment of variant pathogenicity by trained clinical geneticists. The ACMG and AMP developed evidence-based guidelines to standardize variant assessment defining several criteria for supporting evidence of pathogenicity, which are then combined to classify a variant as either pathogenic (P), likely-pathogenic (LP), benign (B), likely-benign (LB), or uncertain significance (VUS). Although widely adopted in clinical interpretation of variants this process has remained largely manual and time-consuming.

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