New Results

DNetPRO: A network approach for low-dimensional signatures from high-throughput data

Nico Curti, Enrico Giampieri, Giuseppe Levi, Gastone Castellani, Daniel Remondini
doi: https://doi.org/10.1101/773622

Article Information

doi 
https://doi.org/10.1101/773622
History 
  • September 19, 2019.
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. Nico Curti1,3,*,
  2. Enrico Giampieri2,
  3. Giuseppe Levi1,3,
  4. Gastone Castellani1,3 and
  5. Daniel Remondini1,3
  1. 1Department of Physics and Astronomy, University of Bologna
  2. 2Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna
  3. 3INFN Bologna
  1. ↵* Corresponding author; email: nico.curti2{at}unibo.it
Back to top
Posted September 19, 2019.
Download PDF

Supplementary Material

Data/Code
Email
DNetPRO: A network approach for low-dimensional signatures from high-throughput data
Nico Curti, Enrico Giampieri, Giuseppe Levi, Gastone Castellani, Daniel Remondini
bioRxiv 773622; doi: https://doi.org/10.1101/773622
Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools

Subject Area