Integrating GWAS, transcriptomics, and machine learning enables genomic prediction of grain cadmium content in malting barley
- DOI
- 10.1016/j.plaphy.2026.111911
- Published
- 2026-11
- Container
- Plant Physiology and Biochemistry
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.plaphy.2026.111911,
title = {Integrating GWAS, transcriptomics, and machine learning enables genomic prediction of grain cadmium content in malting barley},
author = {Chaoqun Xu},
year = {2026},
journal = {Plant Physiology and Biochemistry},
doi = {10.1016/j.plaphy.2026.111911},
url = {https://doi.org/10.1016/j.plaphy.2026.111911}
}RIS
TY - JOUR TI - Integrating GWAS, transcriptomics, and machine learning enables genomic prediction of grain cadmium content in malting barley AU - Chaoqun Xu PY - 2026 JO - Plant Physiology and Biochemistry DO - 10.1016/j.plaphy.2026.111911 UR - https://doi.org/10.1016/j.plaphy.2026.111911 ER -
APA
Xu, C. (2026). Integrating GWAS, transcriptomics, and machine learning enables genomic prediction of grain cadmium content in malting barley. Plant Physiology and Biochemistry. https://doi.org/10.1016/j.plaphy.2026.111911
Source records
- crossref · retrieved 2026-11-05T01:55:51.768Z