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Merging microarray studies to identify a common gene expression signature to several structural heart diseases

dc.contributor.authorFajarda, Olga
dc.contributor.authorDuarte-Pereira, Sara
dc.contributor.authorSilva, Raquel M.
dc.contributor.authorOliveira, José Luís
dc.date.accessioned2021-03-18T17:13:59Z
dc.date.available2021-03-18T17:13:59Z
dc.date.issued2020-07-08
dc.description.abstractBackground: Heart disease is the leading cause of death worldwide. Knowing a gene expression signature in heart disease can lead to the development of more efficient diagnosis and treatments that may prevent premature deaths. A large amount of microarray data is available in public repositories and can be used to identify differentially expressed genes. However, most of the microarray datasets are composed of a reduced number of samples and to obtain more reliable results, several datasets have to be merged, which is a challenging task. The identification of differentially expressed genes is commonly done using statistical methods. Nonetheless, these methods are based on the definition of an arbitrary threshold to select the differentially expressed genes and there is no consensus on the values that should be used. Results: Nine publicly available microarray datasets from studies of different heart diseases were merged to form a dataset composed of 689 samples and 8354 features. Subsequently, the adjusted p-value and fold change were determined and by combining a set of adjusted p-values cutoffs with a list of different fold change thresholds, 12 sets of differentially expressed genes were obtained. To select the set of differentially expressed genes that has the best accuracy in classifying samples from patients with heart diseases and samples from patients with no heart condition, the random forest algorithm was used. A set of 62 differentially expressed genes having a classification accuracy of approximately 95% was identified. Conclusions: We identified a gene expression signature common to different cardiac diseases and supported our findings by showing their involvement in the pathophysiology of the heart. The approach used in this study is suitable for the identification of gene expression signatures, and can be extended to different diseases.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1186/s13040-020-00217-8pt_PT
dc.identifier.eid85087926456
dc.identifier.issn1756-0381
dc.identifier.pmcPMC7346458
dc.identifier.pmid32670412
dc.identifier.urihttp://hdl.handle.net/10400.14/32276
dc.identifier.wos000551795300001
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectGene expression signaturept_PT
dc.subjectHeart diseasept_PT
dc.subjectMicroarray datapt_PT
dc.subjectRandom forestpt_PT
dc.titleMerging microarray studies to identify a common gene expression signature to several structural heart diseasespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue1pt_PT
oaire.citation.titleBioData Miningpt_PT
oaire.citation.volume13pt_PT
person.familyNameFajarda Oliveira
person.familyNameDuarte-Pereira
person.familyNameSilva
person.familyNameOliveira
person.givenNameOlga Margarida
person.givenNameSara
person.givenNameRaquel
person.givenNameJosé Luis
person.identifier121481
person.identifier.ciencia-id9717-5E9C-E002
person.identifier.ciencia-idE519-EACC-96EA
person.identifier.ciencia-id4013-2B06-031F
person.identifier.ciencia-idF716-5261-2F01
person.identifier.orcid0000-0003-1957-4947
person.identifier.orcid0000-0001-5244-068X
person.identifier.orcid0000-0001-5926-8042
person.identifier.orcid0000-0002-6672-6176
person.identifier.ridB-8337-2008
person.identifier.scopus-author-id57191441697
person.identifier.scopus-author-id36774427700
person.identifier.scopus-author-id55209561400
person.identifier.scopus-author-id56375692200
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication6cec1c14-7444-4b2f-9cb6-836c885c6d9e
relation.isAuthorOfPublicationdbc9bd77-6ef1-40d7-876f-3bfa49735792
relation.isAuthorOfPublication.latestForDiscoverydc58d20e-bd0e-4ed9-bf4c-6be15645abaa

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