<p>Evaluation of geo-climactic location-of-origin features for effects on phenotype prediction accuracy with ML models: A case study in <i>Arabidopsis thaliana</i></p>

Elhan Ersoz

doi:10.7490/f1000research.1116446.1

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Ersoz E. Evaluation of geo-climactic location-of-origin features for effects on phenotype prediction accuracy with ML models: A case study in Arabidopsis thaliana [version 1; not peer reviewed]. F1000Research 2019, 8:203 (poster) (https://doi.org/10.7490/f1000research.1116446.1)

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Evaluation of geo-climactic location-of-origin features for effects on phenotype prediction accuracy with ML models: A case study in Arabidopsis thaliana

Elhan Ersoz¹

Published 19 Feb 2019 (https://doi.org/10.7490/f1000research.1116446.1)

Author Affiliations

¹ Umbrella Genetics, USA

Copyright © 2019 Ersoz E. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published 19 Feb 2019

Evaluation of geo-climactic location-of-origin features for effects on phenotype prediction accuracy with ML models: A case study in Arabidopsis thaliana
[version 1; not peer reviewed]

Elhan Ersoz¹

Author Affiliations

¹ Umbrella Genetics, USA

Presented at

Quantitative Genetics and Genomics 2019

Abstract

Competing Interests

Umbrella Genetics is a for-profit organization that provides consulting and analytics services for applications of computational biology for agronomic research. The author is employed by Umbrella Genetics.

Keywords

machine learning, G-by-E, genomic prediction accuracy, environmental features, random forest, classification

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Evaluation of geo-climactic location-of-origin features for effects on phenotype prediction accuracy with ML models: A case study in Arabidopsis thaliana

Evaluation of geo-climactic location-of-origin features for effects on phenotype prediction accuracy with ML models: A case study in Arabidopsis thaliana [version 1; not peer reviewed]

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Evaluation of geo-climactic location-of-origin features for effects on phenotype prediction accuracy with ML models: A case study in Arabidopsis thaliana
[version 1; not peer reviewed]