An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat.
- Submitting institution
-
University of Cambridge
- Unit of assessment
- 12 - Engineering
- Output identifier
- 10665
- Type
- D - Journal article
- DOI
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10.1007/s10994-019-05848-5
- Title of journal
- Mach Learn
- Article number
- -
- First page
- 251
- Volume
- 109
- Issue
- 2
- ISSN
- 0885-6125
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2019
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper comprehensively demonstrated for the first time the effectiveness of machine learning for crop breeding. It led to a collaboration with the International Rice Research Institute (IRRI) in the Philippines (contact head of bioinformatics). IRRI is the main international institute responsible for rice breeding. Rice is the staple food of more than half of the world's population. The technology is also being transferred to UK crop breeding through the UK National Institute of Agricultural Botany (NIAB), the UKs main research organisation in plant science, crop evaluation and agronomy (contact Director of Cambridge crop research).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -