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Autor: Huihui Li
Huihui Li (2014)
Obtain imputed SNP profiles using Beagle v.4 from genotyping-by-sequencing of the accession parents of the SeeD GWAS testcross panel.
Dataset
RNAseq of diverse spring wheat cultivars released during last 110 years
awais rasheed Huihui Li (2023)
Artículo
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RNA SEQUENCE SPRING WHEAT ROOT ARCHITECTURE GENES
Sarah Hearne Edward Buckler Charles Chen Sharon Mitchell Kelly Swarts Huihui Li Jorge Alberto Romero Navarro (2014)
Obtain imputed SNP profiles from genotyping-by-sequencing of the accession parents of the SeeD GWAS testcross panel.
Dataset
Guo-Bo Chen awais rasheed Kai Sonder Cristian Zavala Espinosa Denise Costich Patrick Schnable Sarah Hearne Huihui Li (2019)
This dataset contains the genotypic data obtained using genotyping-by-sequencing (tGBS®) technology (Data2Bio LLC) and the passport data of a total of 1,143 maize accessions, which were collected from 20 countries, including 11 teosinte inbred lines, 764 landraces sampled from the maize collection of the CIMMYT germplasm bank (MGB), 290 CIMMYT elite maize lines (CMLs), and 78 popcorn lines from the USDA Ames inbred collection (Romay et al., 2013).
Dataset
Replication Data for: Multimodal Deep Learning Methods Enhance Genomic Prediction of Wheat Breeding
Carolina Rivera-Amado Francisco Pinto Francisco Javier Pinera-Chavez David González-Diéguez Paulino Pérez-Rodríguez Huihui Li Osval Antonio Montesinos-Lopez Jose Crossa (2023)
In plant breeding research, several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes. To increase the GP accuracy of unobserved phenotypes while simultaneously accounting for the complexity of genotype × environment interaction (GE), deep learning (DL) neural networks have been developed.These analyses can potentially include phenomics data obtained through imaging. The two datasets included in this study contain phenomic, phenotypic, and genotypic data for a set of wheat materials. They have been used to compare a novel DL method with conventional GP models.The results of these analyses are reported in the accompanying journal article.
Dataset
Multimodal deep learning methods enhance genomic prediction of wheat breeding
Carolina Rivera-Amado Francisco Pinto Francisco Javier Pinera-Chavez David González-Diéguez Matthew Paul Reynolds Paulino Pérez-Rodríguez Huihui Li Osval Antonio Montesinos-Lopez Jose Crossa (2023)
Artículo
Conventional Methods Genomic Prediction Accuracy Deep Learning Novel Methods CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT BREEDING MACHINE LEARNING METHODS MARKER-ASSISTED SELECTION
Carolina Sansaloni Jorge Franco Bruno Santos Lawrence Percival-Alwyn Cesar Petroli Jaime Campos Kate Dreher Thomas Payne David Marshall Benjamin Kilian Iain Milne Sebastian Raubach Paul Shaw Gordon Stephen Carolina Saint Pierre Juan Burgueño Jose Crossa Huihui Li Andrzej Kilian Peter Wenzl Ahmed Amri Cristobal Uauy Marianne Bänziger Mario Caccamo Kevin Pixley (2020)
A diverse panel of domesticated hexaploid and tetraploid wheat lines and their tetraploid and diploid wild relatives were genotyped using the DArtSeq technology and characterized in a global wheat diversity analysis.
Dataset