Machine learning meets Monte Carlo methods for models of muscle’s molecular machinery to classify mutations
Anthony Asencio(University of Washington), Farid Moussavi‐Harami(California Institute for Regenerative Medicine), Joseph D. Powers(University of Washington), Jennifer Davis(Cincinnati Children's Hospital Medical Center), Thomas Daniel(University of Washington), Kristina B. Kooiker(California Institute for Regenerative Medicine), Sage Malingen(University of Washington)
Cited by 6
Related Papers
Fibroblast-Specific Genetic Manipulation of p38 Mitogen-Activated Protein Kinase In Vivo Reveals Its Central Regulatory Role in Fibrosis
|Circulation|2017|301
Ablation of cardiac myosin binding protein-C disrupts the super-relaxed state of myosin in murine cardiomyocytes
|Journal of Molecular and Cellular Cardiology|2016|152
Physiological Mitochondrial Fragmentation Is a Normal Cardiac Adaptation to Increased Energy Demand
|Circulation Research|2017|143