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Computational prediction of the human-microbial oral interactome

2014-02-27Journal article Open access
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dc.contributor.authorCoelho, Edgar D.
dc.contributor.authorArrais, Joel P.
dc.contributor.authorMatos, Sérgio
dc.contributor.authorPereira, Carlos
dc.contributor.authorRosa, Nuno
dc.contributor.authorCorreia, Maria J.
dc.contributor.authorBarros, Marlene
dc.contributor.authorOliveira, José L.
dc.date.accessioned2021-07-23T15:35:14Z
dc.date.available2021-07-23T15:35:14Z
dc.date.issued2014-02-27
dc.description.abstractBackground: The oral cavity is a complex ecosystem where human chemical compounds coexist with a particular microbiota. However, shifts in the normal composition of this microbiota may result in the onset of oral ailments, such as periodontitis and dental caries. In addition, it is known that the microbial colonization of the oral cavity is mediated by protein-protein interactions (PPIs) between the host and microorganisms. Nevertheless, this kind of PPIs is still largely undisclosed. To elucidate these interactions, we have created a computational prediction method that allows us to obtain a first model of the Human-Microbial oral interactome.Results: We collected high-quality experimental PPIs from five major human databases. The obtained PPIs were used to create our positive dataset and, indirectly, our negative dataset. The positive and negative datasets were merged and used for training and validation of a naïve Bayes classifier. For the final prediction model, we used an ensemble methodology combining five distinct PPI prediction techniques, namely: literature mining, primary protein sequences, orthologous profiles, biological process similarity, and domain interactions. Performance evaluation of our method revealed an area under the ROC-curve (AUC) value greater than 0.926, supporting our primary hypothesis, as no single set of features reached an AUC greater than 0.877. After subjecting our dataset to the prediction model, the classified result was filtered for very high confidence PPIs (probability ≥ 1-10-7), leading to a set of 46,579 PPIs to be further explored.Conclusions: We believe this dataset holds not only important pathways involved in the onset of infectious oral diseases, but also potential drug-targets and biomarkers. The dataset used for training and validation, the predictions obtained and the network final network are available at http://bioinformatics.ua.pt/software/oralint.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1186/1752-0509-8-24pt_PT
dc.identifier.eid84897570043
dc.identifier.issn1752-0509
dc.identifier.pmcPMC3975954
dc.identifier.pmid24576332
dc.identifier.urihttp://hdl.handle.net/10400.14/34264
dc.identifier.wos000334798700003
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectBayesian classificationpt_PT
dc.subjectOral interactomept_PT
dc.subjectProtein-protein interactionspt_PT
dc.titleComputational prediction of the human-microbial oral interactomept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue1pt_PT
oaire.citation.titleBMC Systems Biologypt_PT
oaire.citation.volume8pt_PT
person.familyNameArrais
person.familyNameMatos
person.familyNameRosa
person.familyNameCorreia
person.familyNameBarros
person.familyNameOliveira
person.givenNameJoel
person.givenNameSérgio
person.givenNameNuno
person.givenNameMaria
person.givenNameMarlene
person.givenNameJosé Luis
person.identifier283619
person.identifierAAG-6405-2021
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person.identifier.orcid0000-0003-4937-2334
person.identifier.orcid0000-0003-1941-3983
person.identifier.orcid0000-0003-4604-0780
person.identifier.orcid0000-0002-6141-9089
person.identifier.orcid0000-0003-0631-4062
person.identifier.orcid0000-0002-6672-6176
person.identifier.ridG-4941-2010
person.identifier.ridA-5627-2013
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person.identifier.scopus-author-id18036537200
person.identifier.scopus-author-id26031510200
person.identifier.scopus-author-id35081186500
person.identifier.scopus-author-id7102145871
person.identifier.scopus-author-id7102895790
person.identifier.scopus-author-id56375692200
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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