A Comprehensive System for Searching and Evaluating Genomic Variant Evidence Using AI and Knowledge Bases to Support Personalized Medicine

Presentation Time: 11:00 AM - 11:15 AM

Abstract Keywords: Bioinformatics, Cancer Genetics, Deep Learning, Precision Medicine, Precision Medicine, Natural Language Processing, Machine Learning, Data Mining
Working Group: Genomics and Translational Bioinformatics Working Group
Primary Track: Applications
Programmatic Theme: Translational Bioinformatics

We introduce an innovative automated system for the search and assessment of genetic variant evidence, meticulously aligned with ACMG guidelines. Leveraging the synergistic power of artificial intelligence (AI), elastic search, and an extensive knowledge base, our system advances the efficiency and accuracy of genetic variant interpretation. Distinct from existing methodologies, it features a pioneering literature filtering mechanism that automates the identification and relevance ranking of scientific articles, significantly reducing the time spending on literature evidence search and optimizing the evidence assessment process. Implemented and rigorously tested by a commercial company hereditary cancer variant curation team, the system demonstrated its effectiveness and scalability by processing over 3 million PMIDs and 1.8 million full-text articles. Throughout the period of active utilization, significant insights were gleaned into the real-world impact and user experience of the system, conclusively affirming its robustness. Our comparative analysis with Mastermind 2.0 highlights the system's enhanced performance in minimizing false positives for various mutation types. The core AI model exhibits exceptional precision, recall, and F1 scores above 0.8, signifying its adeptness in selecting pertinent literature for variant classification. The experience and knowledge acquired from deploying the system in a commercial setting provide a distinctive outlook on its practicality and prospects for future development. The novel integration of AI with traditional genetic variant curation processes heralds a new era in the field, promising significant advancements and broader application prospects.

Speaker(s):
jinlian wang, PhD
UTHealth

Author(s):
Hui Li, Phd - University of Texas Health Science Center at Houston; Hongfang Liu, PhD - University of Texas Health Science Center at Houston;