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Machine learning approach reveals heterogeneous responses to FAK and Rho GTPases inhibition on smooth muscle spheroid formation

Kalyanaraman Vaidyanathan, Chuangqi Wang, Yudong Yu, Amanda Krajnik, Moses Choi, Bolun Lin, John Kolega, View ORCID ProfileKwonmoo Lee, View ORCID ProfileYongho Bae
doi: https://doi.org/10.1101/2020.01.30.927616
Kalyanaraman Vaidyanathan
1Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA
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Chuangqi Wang
2Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA
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Yudong Yu
2Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA
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Amanda Krajnik
1Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA
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Moses Choi
2Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA
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Bolun Lin
3Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA
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John Kolega
1Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA
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Kwonmoo Lee
2Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA
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  • ORCID record for Kwonmoo Lee
  • For correspondence: yonghoba@buffalo.edu klee@wpi.edu
Yongho Bae
1Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA
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  • ORCID record for Yongho Bae
  • For correspondence: yonghoba@buffalo.edu klee@wpi.edu
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Article Information

doi 
https://doi.org/10.1101/2020.01.30.927616
History 
  • January 31, 2020.

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  • You are currently viewing Version 1 of this article (January 31, 2020 - 13:48).
  • View Version 2, the most recent version of this article.
Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Author Information

  1. Kalyanaraman Vaidyanathan1,#,
  2. Chuangqi Wang2,#,
  3. Yudong Yu2,
  4. Amanda Krajnik1,
  5. Moses Choi2,
  6. Bolun Lin3,
  7. John Kolega1,
  8. Kwonmoo Lee2,* and
  9. Yongho Bae1,*
  1. 1Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA
  2. 2Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA
  3. 3Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA
  1. ↵*Correspondence and requests for materials should be addressed to Y.B. (yonghoba{at}buffalo.edu) and K.L. (klee{at}wpi.edu)
  1. ↵# These authors contributed equally: Kalyanaraman Vaidyanathan and Moses Choi.

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Posted January 31, 2020.
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Machine learning approach reveals heterogeneous responses to FAK and Rho GTPases inhibition on smooth muscle spheroid formation
Kalyanaraman Vaidyanathan, Chuangqi Wang, Yudong Yu, Amanda Krajnik, Moses Choi, Bolun Lin, John Kolega, Kwonmoo Lee, Yongho Bae
bioRxiv 2020.01.30.927616; doi: https://doi.org/10.1101/2020.01.30.927616
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Machine learning approach reveals heterogeneous responses to FAK and Rho GTPases inhibition on smooth muscle spheroid formation
Kalyanaraman Vaidyanathan, Chuangqi Wang, Yudong Yu, Amanda Krajnik, Moses Choi, Bolun Lin, John Kolega, Kwonmoo Lee, Yongho Bae
bioRxiv 2020.01.30.927616; doi: https://doi.org/10.1101/2020.01.30.927616

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