Selected article for: "parameter estimation and scale parameter"

Author: Yafei Wang; Randy Heiland; Morgan Craig; Courtney L. Davis; Ashlee N Ford Versypt; Adrianne Jenner; Jonathan Ozik; Nicholson Collier; Chase Cockrell; Andrew Becker; Gary An; James A. Glazier; Aarthi Narayanan; Amber M Smith; Paul Macklin
Title: Rapid community-driven development of a SARS-CoV-2 tissue simulator
  • Document date: 2020_4_5
  • ID: lq4tcyh4_97
    Snippet: We have utilized EMEWS for learning-accelerated exploration of the parameter spaces of agent-based models of immunosurveillance against heterogeneous tumors 91, 92 . The approach allowed for iterative and efficient discovery of optimal control and regression regions within biological and clinical constraints of the multi-scale biological systems. We have applied EMEWS across multiple science domains [101] [102] [103] [104] and developed large-sca.....
    Document: We have utilized EMEWS for learning-accelerated exploration of the parameter spaces of agent-based models of immunosurveillance against heterogeneous tumors 91, 92 . The approach allowed for iterative and efficient discovery of optimal control and regression regions within biological and clinical constraints of the multi-scale biological systems. We have applied EMEWS across multiple science domains [101] [102] [103] [104] and developed large-scale algorithms to improve parameter estimation through approximate Bayesian computation (ABC) approaches 105 . These approaches, applied to the multi-scale modeling of SARS-CoV-2 dynamics, will provide the ability to robustly characterize model behaviors and produce improved capabilities for their interpretation.

    Search related documents:
    Co phrase search for related documents
    • ABC approximate bayesian computation and bayesian computation: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
    • ABC approximate bayesian computation and large scale: 1, 2
    • approximate bayesian computation and bayesian computation: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • approximate bayesian computation and large scale: 1, 2
    • bayesian computation and large scale: 1, 2
    • biological system and large scale: 1, 2, 3, 4, 5, 6, 7, 8
    • improve capability and large scale: 1
    • large scale and learn accelerate: 1
    • large scale and model behavior: 1, 2, 3, 4, 5, 6, 7