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Gene expression analysis of Solanum lycopersicum - Bacillus megaterium Interaction to identify informative genes using machine learning classifiers

dc.contributor.authorRodrigues, Vânia
dc.contributor.authorDeusdado, Sérgio
dc.contributor.authorRodrigues, Vânia
dc.date.accessioned2023-03-14T16:34:09Z
dc.date.available2023-03-14T16:34:09Z
dc.date.issued2022
dc.description.abstractThere has been a growing interest in identifying specific plant growth-promoting rhizobacteria that confer health, growth, and protective benefits to plant host. Understanding the mechanisms of this association as well as the differences that determine the different outcomes can be exploited to optimize beneficial interactions. To this end, we developed a classifier capable of predicting the presence of Bacillus megaterium inoculated in tomato root tissue and identify potential informative genes related to their interaction. Two machine learning models, Kernel Logistic Regression and Multilayer Perceptron were studied. From the 4 Multilayer Perceptron classifiers tested (MLP-a, MLP-b, MLP-c and MLP-d) with different parameters, MLP-a and MLP-c achieved near optimal performance considering all the relevant metrics. Then, these classifiers were used as attribute evaluators to identify two sets of informative genes (IGs). MLP-a showed 216 highest-rated attributes. Among these IGs, 173 were identified as Solanum lycopersicum genes, 37 were assigned to 5 Bacillus subtilis protein, 4 were assigned to 1 Escherichia coli protein and 2 were unidentified. On the other hand, MLP-c showed the same highest-rated attributes adding 27 new attributes. Based on the results of MLP-a and MLP-c, considering the identified tomato IGs, a functional enrichment analysis was developed showing nine and eight biological pathways, respectively. Furthermore, the same IGs were used to compose biological networks from Arabidopsis thaliana orthologous genes. The biological networks identified for the first set were co-expression, shared protein domains, predicted interaction and co-localization. The second set presented the same networks adding physical interaction.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationRodrigues, Vânia; Deusdado, Sérgio (2022). Gene expression analysis of Solanum lycopersicum - Bacillus megaterium Interaction to identify informative genes using machine learning classifiers. In Pereira, Ana I.; Andrej, Košir; Fernandes, Florbela P.; Pacheco, Maria F.; Teixeira, João Paulo; Lopes, Rui Pedro (Eds.). Optimization, Learning Algorithms and Applications: Second International Conference, OL2A 2022. Cham: Springer Nature. p. 427-440. ISBN 978-3-031-23235-0pt_PT
dc.identifier.doi10.1007/978-3-031-23236-7_30pt_PT
dc.identifier.isbn978-3-031-23236-7
dc.identifier.urihttp://hdl.handle.net/10198/27722
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectMachine learningpt_PT
dc.subjectTomato - Bacillus megaterium interactionpt_PT
dc.subjectInformative genespt_PT
dc.subjectMicroarraypt_PT
dc.titleGene expression analysis of Solanum lycopersicum - Bacillus megaterium Interaction to identify informative genes using machine learning classifierspt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.endPage441pt_PT
oaire.citation.startPage427pt_PT
oaire.citation.titleOptimization, Learning Algorithms and Applications: Second International Conference, OL2A 2022pt_PT
oaire.citation.volume1754pt_PT
person.familyNameDeusdado
person.familyNameRodrigues
person.givenNameSérgio
person.givenNameVânia
person.identifier.ciencia-id1D14-2CBC-54F2
person.identifier.ciencia-id6B14-50D7-7EBC
person.identifier.orcid0000-0003-2638-2230
person.identifier.orcid0000-0003-2044-4277
person.identifier.scopus-author-id15764598600
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication1363c41f-0861-40ea-a87a-4a24d9658f03
relation.isAuthorOfPublicationa89e7a00-32c9-4207-a839-c736e6b00952
relation.isAuthorOfPublication.latestForDiscovery1363c41f-0861-40ea-a87a-4a24d9658f03

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