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CINS: Cell Interaction Network inference from Single cell expression data

Ye Yuan, Carlos Cosme Jr., Taylor Sterling Adams, Jonas Schupp, Koji Sakamoto, Nikos Xylourgidis, Matthew Ruffalo, Naftali Kaminski, Ziv Bar-Joseph
doi: https://doi.org/10.1101/2021.02.22.432206

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doi 
https://doi.org/10.1101/2021.02.22.432206
History 
  • February 25, 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-ND 4.0 International license.

Author Information

  1. Ye Yuan1,2,
  2. Carlos Cosme Jr.3,
  3. Taylor Sterling Adams3,
  4. Jonas Schupp3,
  5. Koji Sakamoto3,
  6. Nikos Xylourgidis3,
  7. Matthew Ruffalo4,
  8. Naftali Kaminski3 and
  9. Ziv Bar-Joseph2,4,*,†
  1. 1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
  2. 2Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
  3. 3Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
  4. 4Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
  1. ↵† Corresponding author; email: zivbj{at}andrew.cmu.edu
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Posted February 25, 2021.
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CINS: Cell Interaction Network inference from Single cell expression data
Ye Yuan, Carlos Cosme Jr., Taylor Sterling Adams, Jonas Schupp, Koji Sakamoto, Nikos Xylourgidis, Matthew Ruffalo, Naftali Kaminski, Ziv Bar-Joseph
bioRxiv 2021.02.22.432206; doi: https://doi.org/10.1101/2021.02.22.432206
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