1. Neuroscience

A big-data approach to understanding metabolic rate and response to obesity in laboratory mice

  1. June K Corrigan
  2. Deepti Ramachandran
  3. Yuchen He
  4. Colin J Palmer
  5. Michael J Jurczak
  6. Rui Chen
  7. Bingshan Li
  8. Randall H Friedline
  9. Jason K Kim
  10. Jon J Ramsey
  11. Louise Lantier
  12. Owen P McGuinness
  13. Mouse Metabolic Phenotyping Center Energy Balance Working Group
  14. Alexander Banks  Is a corresponding author
  1. Beth Israel Deaconess Medical Center and Harvard Medical School, United States
  2. University of Pittsburgh School of Medicine, United States
  3. Vanderbilt University School of Medicine, United States
  4. University of Massachusetts Medical School, United States
  5. University of California, Davis, United States
https://doi.org/10.7554/eLife.53560
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  1. June K Corrigan
  2. Deepti Ramachandran
  3. Yuchen He
  4. Colin J Palmer
  5. Michael J Jurczak
  6. Rui Chen
  7. Bingshan Li
  8. Randall H Friedline
  9. Jason K Kim
  10. Jon J Ramsey
  11. Louise Lantier
  12. Owen P McGuinness
  13. Mouse Metabolic Phenotyping Center Energy Balance Working Group
  14. Alexander Banks
(2020)
A big-data approach to understanding metabolic rate and response to obesity in laboratory mice
eLife 9:e53560.
https://doi.org/10.7554/eLife.53560
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  1. June K Corrigan
  2. Deepti Ramachandran
  3. Yuchen He
  4. Colin J Palmer
  5. Michael J Jurczak
  6. Rui Chen
  7. Bingshan Li
  8. Randall H Friedline
  9. Jason K Kim
  10. Jon J Ramsey
  11. Louise Lantier
  12. Owen P McGuinness
  13. Mouse Metabolic Phenotyping Center Energy Balance Working Group
  14. Alexander Banks
(2020)
A big-data approach to understanding metabolic rate and response to obesity in laboratory mice
eLife 9:e53560.
https://doi.org/10.7554/eLife.53560

Share this article

https://doi.org/10.7554/eLife.53560