Open AccessArabidopsis thaliana computationally-generated next-state gene interaction models
Author(s) -
Bree Ann LaPointe,
David John,
James L. Norris,
Alexandria F. Harkey,
Joëlle K. Mühlemann,
Gloria K. Muday
Publication year - 2021
Publication title -
actas del congreso internacional de ingeniería de sistemas
Language(s) - English
Resource type - Journals
ISSN - 2810-806X
DOI - 10.26439/ciis2018.5487
Subject(s) - arabidopsis thaliana , computational biology , arabidopsis , computer science , process (computing) , state (computer science) , gene , data science , biology , genetics , mutant , algorithm , operating system
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 mea-surements, 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.