Variable selection for nonlinear dimensionality reduction of biological datasets through bootstrapping of correlation networks
Aragones D, Palomino-Segura M, Sicilia J, Crainiciuc G, Ballesteros I, Sánchez-Cabo F, Hidalgo A, Calvo G. Variable selection for nonlinear dimensionality reduction of biological datasets through bootstrapping of correlation networks. Computers In Biology And Medicine 2023, 168: 107827. PMID: 38086138, DOI: 10.1016/j.compbiomed.2023.107827.Peer-Reviewed Original ResearchConceptsDimensionality reductionBiological datasetsFast computation timeHigh-dimensional biological datasetsNetwork bootstrappingMachine learningMassive datasetsUnsupervised scenarioNonlinear dimensionality reductionExplainable modelsUnsupervised settingAvailable algorithmsComputation timeCorrelation networkInformative displayInformative onesDatasetComplex systemsVariable selectionElastic netUniform Manifold ApproximationNetworkManifold approximationStandard onesData analysis