Can AI reveal the next generation of high-impact bone genomics targets?
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 cardiovasc...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Bone Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352187225000166 |
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| author | Casey S. Greene Christopher R. Gignoux Marc Subirana-Granés Milton Pividori Stephanie C. Hicks Cheryl L. Ackert-Bicknell |
| author_facet | Casey S. Greene Christopher R. Gignoux Marc Subirana-Granés Milton Pividori Stephanie C. Hicks Cheryl L. Ackert-Bicknell |
| author_sort | Casey S. Greene |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e1b638b4a45e4db5926b734e10a1005f |
| institution | OA Journals |
| issn | 2352-1872 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Bone Reports |
| spelling | doaj-art-e1b638b4a45e4db5926b734e10a1005f2025-08-20T02:07:40ZengElsevierBone Reports2352-18722025-06-012510183910.1016/j.bonr.2025.101839Can AI reveal the next generation of high-impact bone genomics targets?Casey S. Greene0Christopher R. Gignoux1Marc Subirana-Granés2Milton Pividori3Stephanie C. Hicks4Cheryl L. Ackert-Bicknell5Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Corresponding author at: Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USADepartment of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USADepartment of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USADepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA; Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USADepartment of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Colorado Program for Musculoskeletal Research, Department of Orthopedics, University of Colorado School of Medicine, Aurora, CO, USAGenetic 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.http://www.sciencedirect.com/science/article/pii/S2352187225000166Machine learningArtificial intelligenceGeneticsSystems biologyTarget discoveryKnowledge graph |
| spellingShingle | Casey S. Greene Christopher R. Gignoux Marc Subirana-Granés Milton Pividori Stephanie C. Hicks Cheryl L. Ackert-Bicknell Can AI reveal the next generation of high-impact bone genomics targets? Bone Reports Machine learning Artificial intelligence Genetics Systems biology Target discovery Knowledge graph |
| title | Can AI reveal the next generation of high-impact bone genomics targets? |
| title_full | Can AI reveal the next generation of high-impact bone genomics targets? |
| title_fullStr | Can AI reveal the next generation of high-impact bone genomics targets? |
| title_full_unstemmed | Can AI reveal the next generation of high-impact bone genomics targets? |
| title_short | Can AI reveal the next generation of high-impact bone genomics targets? |
| title_sort | can ai reveal the next generation of high impact bone genomics targets |
| topic | Machine learning Artificial intelligence Genetics Systems biology Target discovery Knowledge graph |
| url | http://www.sciencedirect.com/science/article/pii/S2352187225000166 |
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