Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets

Statistical and machine learning (ML)-based methods have recently advanced in construction of gene regulatory network (GRNs) based on high-throughput biological datasets. GRNs underlie almost all cellular phenomena; hence, comprehensive GRN maps are essential tools to elucidate gene function, thereb...

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Detalles Bibliográficos
Autores principales: Mochida, Keiichi, Koda, Satoru, Inoue, Komaki, Nishii, Ryuei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Plant Science
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6281826/
https://www.ncbi.nlm.nih.gov/pubmed/30555503
http://dx.doi.org/10.3389/fpls.2018.01770

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6281826/
https://www.ncbi.nlm.nih.gov/pubmed/30555503
http://dx.doi.org/10.3389/fpls.2018.01770

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