Arabidopsis thaliana computationally-generated next-state gene interaction models

Authors

  • David J. John Bree Wake Forest University. NC, USA
  • Ann LaPointe Wake Forest University. NC, USA
  • James L. Norris Wake Forest University. NC, USA
  • Alexandria F. Harkey Wake Forest University. NC, USA
  • Joëlle K. Muhlemann Wake Forest University. NC, USA
  • Gloria K. Muday Wake Forest University. NC, USA

DOI:

https://doi.org/10.26439/ciis2018.5487

Keywords:

OpenFlow, data center, artificial neural network, Knowledge-Defined Networking

Abstract

The construction of gene interaction models must be a fully collaborative and intentional effort. All aspects of the research, such as growing the plants, extracting the measurements, refining the measured data, developing the statistical framework, and forming and applying the algorithmic techniques, must lend themselves to repeatable and sound practices. This paper holistically focuses on the process of producing gene interaction models based on transcript abundance data from Arabidopsis thaliana after stimulation by a plant hormone.

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References

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Published

2019-07-09

How to Cite

Arabidopsis thaliana computationally-generated next-state gene interaction models. (2019). Actas Del Congreso Internacional De Ingeniería De Sistemas, 17-26. https://doi.org/10.26439/ciis2018.5487