Artificial neural network inference analysis identified novel genes and gene interactions associated with skeletal muscle aging
Using ANNi and RT-qPCR, we identified three strongly interacting genes predicting muscle aging, ZDBF2, USP54, and CHAD. These findings can help to inform the design of nonpharmacological and pharmacological interventions that prevent or mitigate sarcopenia.
Authors
- Lívia Santos (ORCID: https://orcid.org/0000-0002-1915-6780)
- Mikael Altun (ORCID: https://orcid.org/0000-0002-6937-6124)
- Graham Ball (ORCID: https://orcid.org/0000-0001-5828-7129)
- Janelle Tarum (ORCID: https://orcid.org/0009-0007-1690-7191)
- Thomas Gustafsson (ORCID: https://orcid.org/0000-0002-1559-4206)
Institutions
- Karolinska University Hospital (SE)
- Karolinska Institutet (SE)
- Anglia Ruskin University (GB)
- Nottingham Trent University (GB)
Publication Details
- Journal
- Journal of Cachexia Sarcopenia and Muscle
- Published
- 2024-08-29
- DOI
- https://doi.org/10.1002/jcsm.13562
- Citations
- 6
- Primary Topic
- Nutrition and Health in Aging
- Type
- article
- Field-Weighted Citation Impact
- 1.96
Funders
- Nottingham Trent University
- Trent University
- Karolinska Institutet