Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits
This study provides supplemental data to support an investigation of the power of multi-trait deep learning (MTDL) models in terms of genomic-enabled prediction accuracy.
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| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Experimental data biblioteca |
| Language: | English |
| Published: |
CIMMYT Research Data & Software Repository Network
2018
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| Subjects: | Agricultural Sciences, Plant height, Anthesis-silking interval, Maize, Agricultural research, Wheat, Triticum aestivum, Days to heading, Days to maturity, Prediction accuracy, |
| Online Access: | https://hdl.handle.net/11529/10548134 |
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