Predictive Characterization of ICARDAGenebank Barley Accessions sing FIGSand Machine Learning

• Autores: Zainab Azough, Zakaria Kehel, Aziza Benomar, Mostafa Bellafkih, Ahmed Amri
• Localización: Intelligent environments 2019: Workshop Proceedings of the 15th International Conference on Intelligent Environments / coord. por Andrés Muñoz Ortega, Sofia Ouhbi, Wolfgang Minker, Loubna Echabbi, Miguel Navarro Cía, 2019, ISBN 9781614999836, págs. 121-129
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • The International Center for Agricultural Research in the Dry Areas(ICARDA) has a unique germplasm collection of barley, among many other cropsthat it holds in its genebank. This collection contains landraces and barley wildrelatives and most of them are georeferenced. Distribution of genetic resources isa core genebank activity aiming at responding to requests from various users in-cluding breeders, researchers, farmers, etc. ICARDA has developed over the lastdecade an efficient approach for better targeting adaptive traits called the FocusedIdentification of Germplasm Strategy (FIGS). FIGS approach links adaptive traitsto environments (and associated selection pressures) through filtering and machinelearning and it focuses on accessions that are most likely to possess trait specificgenetic variation. In this paper, we present a work of predictive characterization onICARDA barley collection using the FIGS approach and its algorithms combiningseveral machine learning methods, and using several characterization traits. Mostof the studied traits have shown a high predictability. Outcomes from this analysisare then used to make a predictive characterization of the entire ICARDA barleycollection by assigning probabilities of each trait to the non-evaluated accessions.