Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches.

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Bibliographic Details
Title: Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches.
Authors: Chen C; Center for Animal Science, The Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia., Bhuiyan SA; Sugar Research Australia, Woodford, QLD, Australia.; Queensland Micro- and Nanotechnology Centre, Griffith University, Nathan, QLD, Australia., Ross E; Center for Animal Science, The Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia., Powell O; Center for Crop Science, The Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia., Dinglasan E; Center for Animal Science, The Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia., Wei X; Sugar Research Australia, Indooroopilly, QLD, Australia., Atkin F; Sugar Research Australia, Indooroopilly, QLD, Australia., Deomano E; Sugar Research Australia, Indooroopilly, QLD, Australia., Hayes B; Center for Animal Science, The Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia.
Source: Frontiers in plant science [Front Plant Sci] 2024 May 01; Vol. 15, pp. 1398903. Date of Electronic Publication: 2024 May 01 (Print Publication: 2024).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101568200 Publication Model: eCollection Cited Medium: Print ISSN: 1664-462X (Print) Linking ISSN: 1664462X NLM ISO Abbreviation: Front Plant Sci Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1664-462X
DOI:10.3389/fpls.2024.1398903