Expression‐based machine learning models for predicting plant tissue identity

Expression‐based machine learning models for predicting plant tissue identity

Abstract Premise The selection of Arabidopsis as a model organism played a pivotal role in advancing genomic science. The competing frameworks to select an agricultural‐ or ecological‐based model species were rejected, in favor of building knowledge in a species that would facilitate genome‐enabled...

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Main Authors: Sourabh Palande, Jeremy Arsenault, Patricia Basurto‐Lozada, Andrew Bleich, Brianna N. I. Brown, Sophia F. Buysse, Noelle A. Connors, Sikta Das Adhikari, Kara C. Dobson, Francisco Xavier Guerra‐Castillo, Maria F. Guerrero‐Carrillo, Sophia Harlow, Héctor Herrera‐Orozco, Asia T. Hightower, Paulo Izquierdo, MacKenzie Jacobs, Nicholas A. Johnson, Wendy Leuenberger, Alessandro Lopez‐Hernandez, Alicia Luckie‐Duque, Camila Martínez‐Avila, Eddy J. Mendoza‐Galindo, David Cruz Plancarte, Jenny M. Schuster, Harry Shomer, Sidney C. Sitar, Anne K. Steensma, Joanne Elise Thomson, Damián Villaseñor‐Amador, Robin Waterman, Brandon M. Webster, Madison Whyte, Sofía Zorilla‐Azcué, Beronda L. Montgomery, Aman Y. Husbands, Arjun Krishnan, Sarah Percival, Elizabeth Munch, Robert VanBuren, Daniel H. Chitwood, Alejandra Rougon‐Cardoso
Format: Article
Language:English
Published: Wiley 2025-01-01
Series:Applications in Plant Sciences
Subjects:
Arabidopsis
flowering plants
gene expression
machine learning
model species
tissue identity
Online Access:https://doi.org/10.1002/aps3.11621
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https://doi.org/10.1002/aps3.11621

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