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Owen Powell

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Senior Research Fellow

University of Queensland

Australia

Research Interests

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Professors in Australia

Recent Grants

Grant: Open

ARC Training Centre in Predictive Breeding for Agricultural Futures

Open Date: 2024-08-13

Close Date: 2029-08-12

Articles (20)

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Collaborators (12)

Karen Aitken

University of Queensland

AUSTRALIA

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Kathryn Kemper

University of Queensland

AUSTRALIA

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Ben J. Hayes

University of Queensland

AUSTRALIA

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James G Lefevre

Senior Research Fellow

University of Queensland

AUSTRALIA

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Rod Snowdon

Justus Liebig University

GERMANY

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Mark Cooper

Professor in Prediction Based Crop Improvement

University of Queensland

AUSTRALIA

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Lee Hickey

University of Queensland

AUSTRALIA

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Carlos Messina

UNITED STATES

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Shamsul Bhuiyan

Griffith University

AUSTRALIA

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Tim Mahony

Professorial Research Fellow

University of Queensland

AUSTRALIA

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Christie Warburton

The University of Queensland

AUSTRALIA

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Alina Zare

Director

University of Florida

UNITED STATES

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ApplyKite TwinLearns as you apply

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Owen Powell

Research Interests

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Recent Grants

Articles (20)

Better breeding leveraging more biology

Simulating the impact of recombination rate on genomic selection breeding outcomes

Ensemble-based genomic prediction for maize flowering-time improves prediction accuracy and reveals novel insights into trait genetic variation

Ensembles of graph attention networks supervised by genotype-to-phenotype structures improved genomic prediction performance

Optimization of weighted ensembles of genomic prediction models in maize

Genetic linkage map of the Australian barramundi (Lates calcarifer) reveals potential to leverage extreme sex-specific recombination and sequential hermaphrodism for ultimate breeding program control

Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction

Toward a general framework for AI-enabled prediction in crop improvement

Improved genomic prediction performance with ensembles of diverse models

Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP): Computational tool for interpreting ensembles of genomic prediction models

Potential approaches to create ultimate genotypes in crops and livestock

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

Adaptation and plasticity of yield in hybrid and inbred sorghum

Advancing artificial intelligence to help feed the world

Use of continuous genotypes for genomic prediction in sugarcane

Optimising clonal performance in sugarcane: leveraging nonadditive effects via mateallocation strategies

Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs

Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits

Extending the breeder’s equation to take aim at the target population of environments

Collaborators (12)

Karen Aitken

Kathryn Kemper

Ben J. Hayes

James G Lefevre

Rod Snowdon

Mark Cooper

Lee Hickey

Carlos Messina

Shamsul Bhuiyan

Tim Mahony

Christie Warburton

Alina Zare

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