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Tutti frutti: Metabolomics Meets Machine Learning for Juicy Discoveries

hal.structure.identifierBiologie du fruit et pathologie [BFP]
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorPRIGENT, Sylvain
hal.structure.identifierBiologie du fruit et pathologie [BFP]
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorBARROS-SANTOS, Millena
hal.structure.identifierBiologie du fruit et pathologie [BFP]
hal.structure.identifierSchool of Plant Sciences, University of Arizona, Tucson, AZ, 85721
dc.contributor.authorMELANDRI, Giovanni
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorRANDRIAFANOME- ZANTSOA-RADOHERY, Georges
hal.structure.identifierBiologie du fruit et pathologie [BFP]
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorCASSAN, Cédric
hal.structure.identifierBiologie du fruit et pathologie [BFP]
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorGIBON, Yves
hal.structure.identifierBiologie du fruit et pathologie [BFP]
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorPÉTRIACQ, Pierre
dc.date.accessioned2024-08-28T02:03:49Z
dc.date.available2024-08-28T02:03:49Z
dc.date.conference2024-06-25
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/201334
dc.description.abstractEnUnderstanding and predicting fruit phenotypes during development is crucial for quality improvement and food industry applications. Metabolomics, which analyzes the complete set of metabolites within biological samples, is particularly interesting in the case of multi-species studies. It offers a global view of the biochemical processes underlying phenotypes and provides data for many metabolites shared between species, which are therefore interoperable variables. In this study, we combined metabolomics data with machine learning techniques to predict diverse phenotypic traits across the development of ten fruits.By integrating metabolomics profiles with phenotype annotations, we constructed predictive models capable of associating metabolic variable abundance with traits such as growth rate, developmental stage, acidity or sugar content all along fruit development. Supervised machine learning algorithms, including Ridge, Elastic-Net and LASSO regression, Random Forests and Support Vector Machines were used to capture the complex relationships between metabolic profiles and phenotypic variations. Feature selection methods were used to identify key metabolic variables driving the prediction of each phenotype, providing insights into the metabolic functions potentially governing fruit development. Cross-validation procedures and independent validation datasets were employed to assess the robustness and generalization performance of the predictive models.In conclusion, the application of metabolomics on this multispecies datasets represents a significant advancement in our understanding of fruit development and offers unprecedented opportunities for innovation. By combining the power of metabolomics and advanced machine learning techniques, we will be able to unravel intricate molecular mechanisms governing phenotypic traits across multispecies experiments.
dc.description.sponsorshipDéveloppement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation - ANR-11-INBS-0010
dc.description.sponsorshipCentre français de phénomique végétale - ANR-11-INBS-0012
dc.language.isoen
dc.title.enTutti frutti: Metabolomics Meets Machine Learning for Juicy Discoveries
dc.typeAutre communication scientifique (congrès sans actes - poster - séminaire...)
dc.subject.halStatistiques [stat]/Machine Learning [stat.ML]
dc.subject.halInformatique [cs]/Bio-informatique [q-bio.QM]
dc.subject.halSciences du Vivant [q-bio]/Biologie végétale
dc.description.sponsorshipEuropeEuropean Commission’s Horizon 2020 Research and Innovation program via the GLOMICAVE project under grant agreement no. 952908
bordeaux.hal.laboratoriesBiologie du Fruit & Pathologie (BFP) - UMR 1332*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionINRAE
bordeaux.conference.titleJOBIM 2024 - Journées ouvertes en Biologie, Informatique et Mathématiques
bordeaux.countryFR
bordeaux.conference.cityToulouse
bordeaux.peerReviewedoui
hal.identifierhal-04677398
hal.version1
hal.invitednon
hal.proceedingsnon
hal.conference.end2024-06-28
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04677398v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=PRIGENT,%20Sylvain&BARROS-SANTOS,%20Millena&MELANDRI,%20Giovanni&RANDRIAFANOME-%20ZANTSOA-RADOHERY,%20Georges&CASSAN,%20C%C3%A9dric&rft.genre=conference


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