Arabidopsis thaliana computationally-generated next-state gene interaction models

• Autores: LaPointe, Bree, John, David, Norris, James, Harkey, Alexandria, Muhlemann, Joëlle, Muday, Gloria
• Formato: objeto de conferencia
• Fecha de Publicación: 2019
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/8739
• Enlace del recurso: https://hdl.handle.net/20.500.12724/8739
• Nivel de acceso: acceso abierto
• Materia: Redes neuronales artificiales; Arabidopsis thaliana; Artificial neural networks; Genetic transcription; Transcripción genética; Ingeniería de sistemas / Diseño y métodos; http://purl.org/pe-repo/ocde/ford#2.02.04

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.