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dc.creatorMolina Mora, José Arturo
dc.creatorMontero Manso, Pablo
dc.creatorGarcía Batan, Raquel
dc.creatorCampos Sánchez, Rebeca
dc.creatorVilar Fernández, José
dc.creatorGarcía Santamaría, Fernando
dc.date.accessioned2022-03-01T20:15:53Z
dc.date.available2022-03-01T20:15:53Z
dc.date.issued2021
dc.identifier.citationhttps://www.sciencedirect.com/science/article/abs/pii/S0303264721000666?via%3Dihubes_ES
dc.identifier.issn0303-2647
dc.identifier.urihttps://hdl.handle.net/10669/85926
dc.description.abstractTolerance to stress conditions is vital for organismal survival, including bacteria under specific environmental conditions, antibiotics, and other perturbations. Some studies have described common modulation and shared genes during stress response to different types of disturbances (termed as perturbome), leading to the idea of central control at the molecular level. We implemented a robust machine learning approach to identify and describe genes associated with multiple perturbations or perturbome in a Pseudomonas aeruginosa PAO1 model. Using microarray datasets from the Gene Expression Omnibus (GEO), we evaluated six approaches to rank and select genes: using two methodologies, data single partition (SP method) or multiple partitions (MP method) for training and testing datasets, we evaluated three classification algorithms (SVM Support Vector Machine, KNN KNearest neighbor and RF Random Forest). Gene expression patterns and topological features at the systems level were included to describe the perturbome elements. We were able to select and describe 46 core response genes associated with multiple perturbations in P. aeruginosa PAO1 and it can be considered a first report of the P. aeruginosa perturbome. Molecular annotations, patterns in expression levels, and topological features in molecular networks revealed biological functions of biosynthesis, binding, and metabolism, many of them related to DNA damage repair and aerobic respiration in the context of tolerance to stress. We also discuss different issues related to implemented and assessed algorithms, including data partitioning, classification approaches, and metrics. Altogether, this work offers a different and robust framework to select genes using a machine learning approach.es_ES
dc.description.sponsorshipUniversidad de Costa Rica/[803-B8-114]/UCR/Costa Ricaes_ES
dc.description.sponsorshipUniversidad de Costa Rica/[803-B8-152]/UCR/Costa Ricaes_ES
dc.language.isoenges_ES
dc.sourceBiosystems, vol.205, pp.104411.es_ES
dc.subjectPerturbationses_ES
dc.subjectPseudomonas aeruginosaes_ES
dc.subjectMachine learninges_ES
dc.subjectGene selectiones_ES
dc.subjectPerturbomees_ES
dc.titleA first perturbome of Pseudomonas aeruginosa: Identification of core genes related to multiple perturbations by a machine learning approaches_ES
dc.typeartículo científicoes_ES
dc.identifier.doi10.1016/j.biosystems.2021.104411
dc.description.procedenceUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Enfermedades Tropicales (CIET)es_ES
dc.identifier.codproyecto803- B8-114
dc.identifier.codproyecto803-B8-152


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