Finding the combination of multiple biomarkers to diagnose oral squamous cell carcinoma – A data mining approach

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorScience and Technology-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Mogi das Cruzes-
Autor(es): dc.contributorAnhembi Morumbi University-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Paulista-
Autor(es): dc.contributorUniversidade Municipal de São Caetano do Sul-
Autor(es): dc.contributorUniversidade Federal de Goiás (UFG)-
Autor(es): dc.creatorda Costa, Nattane Luíza-
Autor(es): dc.creatorde Sá Alves, Mariana-
Autor(es): dc.creatorde Sá Rodrigues, Nayara-
Autor(es): dc.creatorBandeira, Celso Muller-
Autor(es): dc.creatorOliveira Alves, Mônica Ghislaine-
Autor(es): dc.creatorMendes, Maria Anita-
Autor(es): dc.creatorCesar Alves, Levy Anderson-
Autor(es): dc.creatorAlmeida, Janete Dias-
Autor(es): dc.creatorBarbosa, Rommel-
Data de aceite: dc.date.accessioned2025-08-21T17:05:01Z-
Data de disponibilização: dc.date.available2025-08-21T17:05:01Z-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.compbiomed.2022.105296-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/234108-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/234108-
Descrição: dc.descriptionData mining has proven to be a reliable method to analyze and discover useful knowledge about various diseases, including cancer research. In particular, data mining and machine learning algorithms to study oral squamous cell carcinoma (OSCC), the most common form of oral cancer, is a new area of research. This malignant neoplasm can be studied using saliva samples. Saliva is an important biofluid that must be used to verify potential biomarkers associated with oral cancer. In this study, first, we provide an overview of OSSC diagnoses based on machine learning and salivary metabolites. To our knowledge, this is the first study to apply advanced data mining techniques to diagnose OSCC. Then, we give new results of classification and feature selection algorithms used to identify potential salivary biomarkers of OSCC. To accomplish this task, we used the filter feature selection random forest importance algorithm and a wrapper methodology to evaluate the importance of metabolites obtained from gas chromatography mass-spectrometry (GC-MS) in the context of differentiation of OSCC and the control group. Salivary samples (n = 68) were collected for the control group, and the OSCC group were from patients matched for gender, age, and smoking habit. The classification process occurred based on Random Forest (RF) classification algorithm along with 10-cross validation. The results showed that glucuronic acid, maleic acid, and batyl alcohol can classify the samples with an area under the curve (AUC) of 0.91 versus an AUC of 0.76 using all 51 metabolites analyzed. The methodology used in this study can assist healthcare professionals and be adopted to discover diagnostic biomarkers for other diseases.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionInformatics Nucleo Goiano Federal Institute of Education Science and Technology, Campus Urutaí-
Descrição: dc.descriptionDepartment of Biosciences and Oral Diagnosis Institute of Science and Technology São Paulo State University (Unesp)-
Descrição: dc.descriptionTechnology Reaearch Center (NPT) Universidade Mogi das Cruzes-
Descrição: dc.descriptionSchool of Medicine Anhembi Morumbi University-
Descrição: dc.descriptionDempster MS Lab Universidade de São Paulo-
Descrição: dc.descriptionSchool of Dentistry Universidade Paulista-
Descrição: dc.descriptionSchool of Dentistry Universidade Municipal de São Caetano do Sul-
Descrição: dc.descriptionInstituto de Informática Universidade Federal de Goiás-
Descrição: dc.descriptionDepartment of Biosciences and Oral Diagnosis Institute of Science and Technology São Paulo State University (Unesp)-
Descrição: dc.descriptionFAPESP: 2016/08633-0-
Idioma: dc.languageen-
Relação: dc.relationComputers in Biology and Medicine-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectData mining-
Palavras-chave: dc.subjectFeature selection-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectMetabolites-
Palavras-chave: dc.subjectOral squamous cell carcinoma-
Palavras-chave: dc.subjectSalivary biomarkers-
Título: dc.titleFinding the combination of multiple biomarkers to diagnose oral squamous cell carcinoma – A data mining approach-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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