Immune/neural approach to characterize salivary gland neoplasms (SGN)

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dc.creatorCarlos Rafael Lima Monção
dc.creatorEloa Mangabeira Santos
dc.creatorThiago Silva Prates
dc.creatorAlfredo Maurício Batista de Paula
dc.creatorCláudio Marcelo Cardoso
dc.creatorLucyana Conceição Farias
dc.creatorSérgio Henrique Sousa Santos
dc.creatorMarcos Flávio Silveira Vasconcelos D’Angelo
dc.creatorAndré Luiz Sena Guimarães
dc.date.accessioned2022-09-05T13:10:32Z
dc.date.accessioned2025-09-09T00:05:09Z
dc.date.available2022-09-05T13:10:32Z
dc.date.issued2020-03
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2019.105877
dc.identifier.issn1568-4946
dc.identifier.urihttps://hdl.handle.net/1843/44899
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofApplied Soft Computing
dc.rightsAcesso Restrito
dc.subjectTumores
dc.subjectGlândulas salivares
dc.subjectBioinformática
dc.titleImmune/neural approach to characterize salivary gland neoplasms (SGN)
dc.typeArtigo de periódico
local.citation.spage105877
local.citation.volume88
local.description.resumoThe purpose of the current study was to use immune-inspired algorithm ClonALG whose performance is increased by using the Kohonen neural network training algorithm (Immune/Neural approach), to characterize the nature of salivary gland neoplasms (SGNs). The leader gene approach in order to identify biomarkers for SGNs. Extensive data were obtained for each of the 35 types of neoplasms. The gene leaders for each type of SGN were identified in a table and then divided according to the two different methods: K-means clustering and Immune/Neural approach. Genes related to SGNs were identified using PubMed, OMIM and Genecards databases. A bioinformatics algorithm was then applied, and the STRING database was employed to build networks of protein–protein interactions for each nature of an SGNs. The weighted number of links (WNL) and total interactions score (TIS) values were then obtained. Finally, the genes were clustered, and the gene leaders were identified using the K-means clustering method and the Immune/Neural approach.
local.publisher.countryBrasil
local.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S1568494619306581

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