Can AI reveal the next generation of high-impact bone genomics targets?

Journal: Bone reports
Published Date: Mar 24, 2025

Abstract

Genetic studies have revealed hundreds of loci associated with bone-related phenotypes, including bone mineral density (BMD) and fracture risk. However, translating discovered loci into effective new therapies remains challenging. We review success stories including PCSK9-related drugs in cardiovascular disease and evidence supporting the use of human genetics to guide drug discovery, while highlighting advances in artificial intelligence and machine learning with the potential to improve target discovery in skeletal biology. These strategies are poised to improve how we integrate diverse data types, from genetic and electronic health records data to single-cell profiles and knowledge graphs. Such emerging computational methods can position bone genomics for a future of more precise, effective treatments, ultimately improving the outcomes for patients with common and rare skeletal disorders.

Authors

  • Casey S Greene
    Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, United States; Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, United States; Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Perelman School of Medicine, University of Pennsylvania, United States. Electronic address: csgreene@upenn.edu.
  • Christopher R Gignoux
    Colorado Center for Personalized Medicine, University of Colorado-Anschutz, Aurora, CO 80045, USA.
  • Marc Subirana-GranĂ©s
    Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
  • Milton Pividori
    sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.
  • Stephanie C Hicks
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. shicks19@jhu.edu.
  • Cheryl L Ackert-Bicknell
    Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals