Work

Learning Equations from Biological Data with Limited Time Samples

Year: 2020

Type: article

Source: Bulletin of Mathematical Biology

DOI: 10.1007/s11538-020-00794-z

Authors: John T. Nardini, John H. Lagergren, Andrea Hawkins-Daarud, Lee Curtin, Bethan Morris, Erica M. Rutter, Kristin R. Swanson, Kevin B. Flores

Institutions: Statistical and Applied Mathematical Sciences Institute, North Carolina State University, Mayo Clinic, University of Nottingham, University of California, Merced

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, James S. McDonnell Foundation, National Institutes of Health, Engineering and Physical Sciences Research Council, National Institute on Aging, National Cancer Institute, Division of Mathematical Sciences

Awards: RoL: FELS: EAGER Rules of lifespan determination and buffering from lifelong spatiotemporal activity of key aging pathways., G3561834706, G3619593601, Statistical and Applied Mathematical Sciences Institute, Modelling the Heterogeneity of Glioblastoma, G5985085362, A platform for in vivo lifelong tracking of aging genes and lifespan prediction under precise environments in C. elegans., G6996712817, Quantifying Multiscale Competitive Landscapes of Clonal Diversity in Glioblastoma, DTP 2016-2017 University of Nottingham

OpenAlex ID: W3026064596

FWCI: 1.48

Cites: 53

Cited by: 31

Related to: 10