A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis

Gene regulatory networks (GRNs) provide a representation of relationships between regulators and their target genes. Several methods for GRN inference, both unsupervised and supervised, have been developed to date. Because regulatory relationships consistently reprogram in diverse tissues or under d...

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Detalles Bibliográficos
Autores principales: Ni, Ying, Aghamirzaie, Delasa, Elmarakeby, Haitham, Collakova, Eva, Li, Song, Grene, Ruth, Heath, Lenwood S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Plant Science
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5179539/
https://www.ncbi.nlm.nih.gov/pubmed/28066488
http://dx.doi.org/10.3389/fpls.2016.01936

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5179539/
https://www.ncbi.nlm.nih.gov/pubmed/28066488
http://dx.doi.org/10.3389/fpls.2016.01936

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