Cargando…
Comparing Deep Learning Approaches for Understanding Genotype × Phenotype Interactions in Biomass Sorghum
We explore the use of deep convolutional neural networks (CNNs) trained on overhead imagery of biomass sorghum to ascertain the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. We consider both CNNs trained explicitly on the cla...
Autores principales: | Zhang, Zeyu, Pope, Madison, Shakoor, Nadia, Pless, Robert, Mockler, Todd C., Stylianou, Abby |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9289439/ https://www.ncbi.nlm.nih.gov/pubmed/35860344 http://dx.doi.org/10.3389/frai.2022.872858 |
Ejemplares similares
-
Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest
por: Johansen, Kasper, et al.
Publicado: (2020) -
An autoencoder-based deep learning method for genotype imputation
por: Song, Meng, et al.
Publicado: (2022) -
Explainable deep learning in plant phenotyping
por: Mostafa, Sakib, et al.
Publicado: (2023) -
Deep Learning for Understanding Satellite Imagery: An Experimental Survey
por: Mohanty, Sharada Prasanna, et al.
Publicado: (2020) -
Gamma and vega hedging using deep distributional reinforcement learning
por: Cao, Jay, et al.
Publicado: (2023)