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Differential Methods for Assessing Sensitivity in Biological Models

View ORCID ProfileRachel Mester, View ORCID ProfileAlfonso Landeros, Chris Rackauckas, Kenneth Lange
doi: https://doi.org/10.1101/2021.11.15.468697
Rachel Mester
1Departments of Computational Medicine, University of California Los Angeles, Los Angeles, CA 90095
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Alfonso Landeros
1Departments of Computational Medicine, University of California Los Angeles, Los Angeles, CA 90095
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Chris Rackauckas
4Massachusetts Institute of Technology
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Kenneth Lange
1Departments of Computational Medicine, University of California Los Angeles, Los Angeles, CA 90095
2Departments of Human Genetics, University of California Los Angeles, Los Angeles, CA 90095
3Departments of Statistics, University of California Los Angeles, Los Angeles, CA 90095
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  • For correspondence: klange@ucla.edu
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Article Information

doi 
https://doi.org/10.1101/2021.11.15.468697
History 
  • November 17, 2021.
Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

Author Information

  1. Rachel Mester1,
  2. Alfonso Landeros1,
  3. Chris Rackauckas4,5 and
  4. Kenneth Lange1,2,3,*
  1. 1Departments of Computational Medicine, University of California Los Angeles, Los Angeles, CA 90095
  2. 2Departments of Human Genetics, University of California Los Angeles, Los Angeles, CA 90095
  3. 3Departments of Statistics, University of California Los Angeles, Los Angeles, CA 90095
  4. 4Massachusetts Institute of Technology
  5. 5Pumas-AI
  1. ↵* Corresponding author; email: klange{at}ucla.edu, Phone: 310-206-8076
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Posted November 17, 2021.
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Differential Methods for Assessing Sensitivity in Biological Models
Rachel Mester, Alfonso Landeros, Chris Rackauckas, Kenneth Lange
bioRxiv 2021.11.15.468697; doi: https://doi.org/10.1101/2021.11.15.468697
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Differential Methods for Assessing Sensitivity in Biological Models
Rachel Mester, Alfonso Landeros, Chris Rackauckas, Kenneth Lange
bioRxiv 2021.11.15.468697; doi: https://doi.org/10.1101/2021.11.15.468697

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