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    Home»AI & Automation»$12.8m grant to lead initiative to get genomics toward the clinic via machine learning and AI  
    AI & Automation

    $12.8m grant to lead initiative to get genomics toward the clinic via machine learning and AI  

    myappsplusBy myappsplusOctober 6, 2026003 Mins Read
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    .8m grant to lead initiative to get genomics toward the clinic via machine learning and AI  
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    With two research grants projected to total $12.8 million over five years, Vanderbilt Health will both lead and participate as a development site in a research consortium aimed at translating genomic research findings into clinical applications through new use of machine learning and artificial intelligence (AI).   

    Established by the National Institutes of Health (NIH), the consortium is called Machine Learning/Artificial Intelligence Tools to Advance Genomic Translational Research, orMAGen. Bradley Malin, PhD, the Accenture Professor and Professor of Biomedical Informatics, Biostatistics and Computer Science, will establish and lead the consortium’s coordinating center at Vanderbilt with the aid of a five-year phased innovation award from the NIH.   

    Under a separate five-year NIH grant, Lisa Bastarache, MS, Research Associate Professor of Biomedical Informatics, will establish one of the consortium’s three development sites. Other consortium sites are at Massachusetts General Hospital in Boston and Icahn School of Medicine at Mount Sinai in New York City.  

    Under NIH phased innovation awards, funding for years three to five is contingent on achieving project milestones in years one and two. Malin’s two-year consortium coordinating center grant totals $1.2 million, with funding for years three to five projected to be an additional $3.6 million.  

    Bastarache received a phased innovation award of $3.2 million for years one and two, with funding for years three to five projected to be an additional $4.8 million.   

    “While decades of research have brought to light myriad associations of genetic variation with all manner of disease, many promised advances in genomic medicine and personalized treatment await a deepened understanding of how pathogenic variants become manifest in individuals,” Malin said. “Machine learning and artificial intelligence carry tremendous promise for powering this critical field of inquiry, bringing new precision to bear on these questions, and ultimately rendering more genomic information actionable in the clinic. Our center at Vanderbilt will support the consortium to create generalizable and accessible tools to predict and explain how variants cause disease in individuals.”  

    Malin serves as contact principal investigator and project leader for the coordinating center, which is called GenePath, or Genetic variant Evidence using Novel tools to Elucidate Pathophysiology: Accelerating Translation to Health. He’s joined in the project by fellow principal investigatorsJada Benn Torres, PhD, Associate Professor of Anthropology, and Wei-Qi Wei, MD, PhD, Professor of Biomedical Informatics.  

    GenePath will convene Vanderbilt experts in variant interpretation, protein function, genomic medicine, genetic anthropology, informatics, health equity, genomic consortium coordination, and ethical, legal, and social implications (ELSI) research. Components of the coordinating center will include a scientific operations unit, a data and machine learning unit to create a common data model and plan cross-validation protocols, and an engagement in ELSI unit to ensure communication with patients, families, communities, and providers to guide consortium planning.  

    MAGen’s current development sites are:  

    • Ethically guided multimodal AI models for predicting disease pathogenesis in individuals with pathogenic variants, led by Konrad Karczewski, PhD, of Harvard University and Massachusetts General Hospital.  
    • Genomics-empowered AI for personalized cancer risk assessment, monitoring, and prevention, led by Kuan-Lin Huang, PhD, of Icahn School of Medicine at Mount Sinai.  
    • Patient-centered prediction of clinically important outcomes arising from pathogenic variants, led by Bastarache at Vanderbilt Health.   

    GenePath is supported by NIH grant 1UG3HG014374, Bastarache’s development site by NIH grant 1UG3HG014376.  

    128m genomics grant initiative lead
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