{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13"],"submitter":["Devarajan AK"],"pubmed_abstract":["Agricultural application of plant-beneficial bacteria to improve crop yield and alleviate the stress caused by environmental conditions, pests, and pathogens is gaining popularity. However, before using these bacterial strains in plant experiments, their environmental stress responses and plant health improvement potential should be examined. In this study, we explored the applicability of three unsupervised machine learning-based data integration methods, including principal component analysis (PCA) of concatenated data, multiple co-inertia analysis (MCIA), and multiple kernel learning (MKL), to select osmotic stress-tolerant plant growth-promoting (PGP) bacterial strains isolated from the rice phyllosphere. The studied datasets consisted of direct and indirect PGP activity measurements a"],"journal":["Frontiers in microbiology"],"pagination":["1058772"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9797599"],"repository":["biostudies-literature"],"pubmed_title":["Application of data integration for rice bacterial strain selection by combining their osmotic stress response and plant growth-promoting traits."],"pmcid":["PMC9797599"],"pubmed_authors":["Muthukrishanan G","Gopalasubramaniam SK","Truu J","Devarajan AK","Truu M"],"additional_accession":[]},"is_claimable":false,"name":"Application of data integration for rice bacterial strain selection by combining their osmotic stress response and plant growth-promoting traits.","description":"Agricultural application of plant-beneficial bacteria to improve crop yield and alleviate the stress caused by environmental conditions, pests, and pathogens is gaining popularity. However, before using these bacterial strains in plant experiments, their environmental stress responses and plant health improvement potential should be examined. In this study, we explored the applicability of three unsupervised machine learning-based data integration methods, including principal component analysis (PCA) of concatenated data, multiple co-inertia analysis (MCIA), and multiple kernel learning (MKL), to select osmotic stress-tolerant plant growth-promoting (PGP) bacterial strains isolated from the rice phyllosphere. The studied datasets consisted of direct and indirect PGP activity measurements a","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2026-04-08T11:13:41.93Z","creation":"2024-11-13T02:23:46.562Z"},"accession":"S-EPMC9797599","cross_references":{"pubmed":["36590400"],"doi":["10.3389/fmicb.2022.1058772"]}}