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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/61668
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dc.creatorFabiano de Oliveira Poswarpt_BR
dc.creatorLaércio Ives Santospt_BR
dc.creatorLucyana Conceição Fariaspt_BR
dc.creatorTalita Antunes Guimarãespt_BR
dc.creatorSérgio Henrique Sousa Santospt_BR
dc.creatorKimberly Marie Jonespt_BR
dc.creatorAlfredo Maurício Batista de Paulapt_BR
dc.creatorReinaldo Martinez Palharespt_BR
dc.creatorMarcos Flávio Silveira Vasconcelos D'Angelopt_BR
dc.creatorAndré Luiz Sena Guimarãespt_BR
dc.date.accessioned2023-12-04T12:55:22Z-
dc.date.available2023-12-04T12:55:22Z-
dc.date.issued2017-06-
dc.citation.volume12pt_BR
dc.citation.spage72pt_BR
dc.citation.epage77pt_BR
dc.identifier.doihttps://doi.org/10.1016/j.mgene.2017.01.007pt_BR
dc.identifier.issn2214-5400pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/61668-
dc.description.resumoIntroduction: this study used the comparison of basal cell carcinoma (BCC), squamous cell carcinoma of the skin (SCC) and actinic keratosis (AK) to test a new method for data set clustering in the leader gene approach. Methods: genes related to BCC, SCC and AK, were identified in the databases: OMIM, Genecards and NCBI Gene. A network was built for BCC, SCC and AK using STRING. For each gene, a weighted number of links (WNL) was calculated based on the combined STRING scores. The genes were then clustered according to their WNL and TIS, using an adaptation of particle swarm clustering (PSC) or K-means clustering. Results: a disagreement between K-means clustering and PSC was observed for both BCC and SCC. PSC suggested completed different leader genes to BCC and SCC. While K-means clustering indicated that CTNNB1 and TP53 were associated with BCC and SCC. In contrast, no differences in methods were observed to AK, which had the shorter network. TP53 was the only leader gene for AK. Conclusion: in conclusion, the current study suggests that PSC is an interesting tool for clustering genes in bioinformatics analyses of prevalent diseases. K-means clustering should be used in the small network. The current study also suggests TP53 may play a central role for AK. Additionally, CTNNB1 seems to be related to BCC, while CTNNA1 is related to SCC.pt_BR
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológicopt_BR
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Geraispt_BR
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICApt_BR
dc.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIASpt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofMeta Gene-
dc.rightsAcesso Restritopt_BR
dc.subjectParticle swarm clusteringpt_BR
dc.subjectBioinformaticspt_BR
dc.subjectSkin cancerspt_BR
dc.subjectPotential malignant lesionpt_BR
dc.subjectMetastasispt_BR
dc.subject.otherCarcinoma basocelularpt_BR
dc.subject.otherCarcinoma de células escamosaspt_BR
dc.subject.otherPele - Câncerpt_BR
dc.subject.otherBioinformáticapt_BR
dc.subject.otherCeratosept_BR
dc.titleAn adaptation of particle swarm clustering applied in basal cell carcinoma, squamous cell carcinoma of the skin and actinic keratosispt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.sciencedirect.com/science/article/pii/S2214540017300087pt_BR
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