Meta-learning approach for bacteria classification and identification of informative genes of the Bacillus megaterium: tomato roots tissue interaction

Rodrigues, Vânia; Deusdado, Sérgio

Publication

Meta-learning approach for bacteria classification and identification of informative genes of the Bacillus megaterium: tomato roots tissue interaction

2023Journal article

dc.contributor.author	Rodrigues, Vânia
dc.contributor.author	Deusdado, Sérgio
dc.contributor.author	Rodrigues, Vânia
dc.date.accessioned	2023-07-18T11:06:02Z
dc.date.available	2023-07-18T11:06:02Z
dc.date.issued	2023
dc.description.abstract	Plant growth-promoting rhizobacteria (PGPRs) are bacteria that colonize the plant roots. These beneficial bacteria have an influence on plant development through multiple mechanisms, such as nutrient availability, alleviating biotic and abiotic stress, and secrete phytohormones. Therefore, their inoculation constitutes a powerful tool towards sustainable agriculture and crop production. To understand plant-PGPRs interaction we present the classification of PGPR using machine learning and meta-learning classifiers namely Support Vector Machine (SVM), Kernel Logistic Regression (KLR), meta-SVM and meta-KLR to predict the presence of Bacillus megaterium inoculated in tomato root tissues using publicly available transcriptomic data. The original dataset presents 36 significantly differentially expressed genes. As the meta-KLR achieved near-optimal performance considering all the relevant metrics, this meta learner was afterwards used to identify the informative genes (IGs). The outcomes showed 157 IGs, being present all significantly differentially expressed genes previously identified. Among the IGs, 113 were identified as tomato genes, 5 as Bacillus subtilis proteins, 1 as Escherichia coli protein and 6 were unidentified. Then, a functional enrichment analysis of the tomato IGs showed 175 biological processes, 22 molecular functions and 20 KEGG pathways involved in B. megaterium–tomato interaction. Furthermore, the biological networks study of their Arabidopsis thaliana orthologous genes identified the co-expression, predicted interaction, shared protein domains and co-localization networks.	pt_PT
dc.description.sponsorship	Open access funding provided by FCT\|FCCN (b-on)	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Rodrigues, Vânia; Deusdado, Sérgio (2023). Meta-learning approach for bacteria classification and identification of informative genes of the Bacillus megaterium: tomato roots tissue interaction. 3 BIOTECH. ISSN 2190-572X. 13:8, p. 1-12	pt_PT
dc.identifier.doi	10.1007/s13205-023-03690-0	pt_PT
dc.identifier.issn	2190-572X
dc.identifier.uri	http://hdl.handle.net/10198/28565
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Springer Nature	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Machine learning	pt_PT
dc.subject	Meta learners	pt_PT
dc.subject	Informative genes	pt_PT
dc.subject	Solanum lycopersicum	pt_PT
dc.subject	Bacillus megaterium	pt_PT
dc.title	Meta-learning approach for bacteria classification and identification of informative genes of the Bacillus megaterium: tomato roots tissue interaction	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.issue	8	pt_PT
oaire.citation.title	3 Biotech	pt_PT
oaire.citation.volume	13	pt_PT
person.familyName	Deusdado
person.familyName	Rodrigues
person.givenName	Sérgio
person.givenName	Vânia
person.identifier.ciencia-id	1D14-2CBC-54F2
person.identifier.ciencia-id	6B14-50D7-7EBC
person.identifier.orcid	0000-0003-2638-2230
person.identifier.orcid	0000-0003-2044-4277
person.identifier.scopus-author-id	15764598600
rcaap.rights	openAccess	pt_PT
rcaap.type	article	pt_PT
relation.isAuthorOfPublication	1363c41f-0861-40ea-a87a-4a24d9658f03
relation.isAuthorOfPublication	a89e7a00-32c9-4207-a839-c736e6b00952
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