Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/43009
DC FieldValueLanguage
dc.contributor.authorHarfouche, Antoineit
dc.contributor.authorJacobson, Daniel Ait
dc.contributor.authorKainer, Davidit
dc.contributor.authorRomero, Jonathon Cit
dc.contributor.authorScarascia Mugnozza, Giuseppeit
dc.contributor.authorMoshelion, Menachemit
dc.contributor.authorTuskan, Gerald Ait
dc.contributor.authorKeurentjes, Joost J Bit
dc.contributor.authorAltman, Arieit
dc.contributor.authorHarfouche, Antoine Hit
dc.date.accessioned2021-02-25T07:35:11Z-
dc.date.available2021-02-25T07:35:11Z-
dc.date.issued2019it
dc.identifier.issn1538-344Xit
dc.identifier.urihttp://hdl.handle.net/2067/43009-
dc.description.abstractBreeding crops for high yield and superior adaptability to new and variable climates is imperative to ensure continued food security, biomass production, and ecosystem services. Advances in genomics and phenomics are delivering insights into the complex biological mechanisms that underlie plant functions in response to environmental perturbations. However, linking genotype to phenotype remains a huge challenge and is hampering the optimal application of high-throughput genomics and phenomics to advanced breeding. Critical to success is the need to assimilate large amounts of data into biologically meaningful interpretations. Here, we present the current state of genomics and field phenomics, explore emerging approaches and challenges for multiomics big data integration by means of next-generation (Next-Gen) artificial intelligence (AI), and propose a workable path to improvement.it
dc.titleAccelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligenceit
dc.typearticle-
dc.identifier.doi10.1016/j.tibtech.2019.05.007it
dc.identifier.pmid31235329it
dc.identifier.scopus2-s2.0-85067510759it
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85067510759it
dc.relation.issn0167-7799-
dc.relation.journalCELL PRESERVATION TECHNOLOGYit
dc.relation.firstpage1217-1235it
dc.relation.lastpage1235it
dc.relation.numberofpages18it
dc.relation.volume37it
dc.relation.issue11it
dc.contributor.countryITAit
dc.type.refereeREF_1it
dc.type.miur262-
local.miur.syncfalse-
item.cerifentitytypePublications-
item.openairetypearticle-
item.fulltextWith Fulltext-
item.grantfulltextrestricted-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.journal.journalissn1538-344X-
crisitem.journal.anceE200898-
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