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Plant breeding has become a data science. Advanced phenotyping platforms and next-generation sequencing technologies including mass sequencing of genomes and transcriptomes, drive this revolution in plant breeding. Furthermore, cost-effective genomic resources, including the Eucalypt and Pine Axiom SNP arrays, will lead to the development of large amounts of genomic data in the next decade (100,000s of trees). As a result, the FMG Population Genomics Team is investing in data science approaches to organize, store and analyse the various different types of data that we work with, as well as in machine learning and artificial intelligence (AI) approaches to integrate genome, phenome and environment data. Current research focus areas include combining systems genetics and AI to dissect the regulatory pathways of wood formation in Eucalyptus; using machine learning approaches to predict lumber yield and lumber quality from Pine growing conditions; and simulating breeding populations to apply machine learning in genomic selection.


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