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Estimating Drivers of Cell State Transitions Using Gene Regulatory Network Models
Daniel Schlauch, Kimberly Glass, Craig P. Hersh, Edwin K. Silverman, John Quackenbush
doi: https://doi.org/10.1101/089003
Daniel Schlauch
aDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA 02115
bChanning Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115
Kimberly Glass
bChanning Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115
cDepartment of Medicine, Harvard Medical School, Boston, MA 02115
Craig P. Hersh
bChanning Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115
cDepartment of Medicine, Harvard Medical School, Boston, MA 02115
dPulmonary and Critical Care Division, Brigham and Women’s Hospital and Harvard Medical School, Boston, USA
Edwin K. Silverman
bChanning Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115
cDepartment of Medicine, Harvard Medical School, Boston, MA 02115
dPulmonary and Critical Care Division, Brigham and Women’s Hospital and Harvard Medical School, Boston, USA
John Quackenbush
aDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA 02115
bChanning Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115
cDepartment of Medicine, Harvard Medical School, Boston, MA 02115
Article usage
Posted November 21, 2016.
Estimating Drivers of Cell State Transitions Using Gene Regulatory Network Models
Daniel Schlauch, Kimberly Glass, Craig P. Hersh, Edwin K. Silverman, John Quackenbush
bioRxiv 089003; doi: https://doi.org/10.1101/089003
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