Repository logo
 
Publication

An analysis of protein patterns present in the saliva of diabetic patients using pairwise relationship and hierarchical clustering

dc.contributor.authorSoares, Airton
dc.contributor.authorEsteves, Eduardo
dc.contributor.authorRosa, Nuno
dc.contributor.authorEsteves, Ana Cristina
dc.contributor.authorLins, Anthony
dc.contributor.authorBastos-Filho, Carmelo J. A.
dc.date.accessioned2021-03-20T18:34:55Z
dc.date.available2021-11-03T01:30:20Z
dc.date.issued2020
dc.description.abstractMolecular diagnosis is based on the quantification of RNA, proteins, or metabolites whose concentration can be correlated to clinical situations. Usually, these molecules are not suitable for early diagnosis or to follow clinical evolution. Large-scale diagnosis using these types of molecules depends on cheap and preferably noninvasive strategies for screening. Saliva has been studied as a noninvasive, easily obtainable diagnosis fluid, and the presence of serum proteins in it enhances its use as a systemic health status monitoring tool. With a recently described automated capillary electrophoresis-based strategy that allows us to obtain a salivary total protein profile, it is possible to quantify and analyze patterns that may indicate disease presence or absence. The data of 19 persons with diabetes and 58 healthy donors obtained by capillary electrophoresis were transformed, treated, and grouped so that the structured values could be used to study individuals’ health state. After Pairwise Relationships and Hierarchical Clustering analysis were observed that amplitudes of protein peaks present in the saliva of these individuals could be used as differentiating parameters between healthy and unhealthy people. It indicates that these characteristics can serve as input for a future computational intelligence algorithm that will aid in the stratification of individuals that manifest changes in salivary proteins.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.doi10.1007/978-3-030-62362-3_14pt_PT
dc.identifier.eid85097416308
dc.identifier.isbn9783030623616
dc.identifier.isbn9783030623623
dc.identifier.urihttp://hdl.handle.net/10400.14/32304
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectCapillary electrophoresispt_PT
dc.subjectClustering algorithmspt_PT
dc.subjectData miningpt_PT
dc.subjectDiagnosispt_PT
dc.subjectSalivapt_PT
dc.titleAn analysis of protein patterns present in the saliva of diabetic patients using pairwise relationship and hierarchical clusteringpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage159pt_PT
oaire.citation.startPage148pt_PT
oaire.citation.titleIntelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedingspt_PT
person.familyNameSoares
person.familyNameEsteves
person.familyNameRosa
person.familyNameEsteves
person.familyNameLins
person.familyNameBastos-Filho
person.givenNameAirton
person.givenNameEduardo
person.givenNameNuno
person.givenNameAna Cristina
person.givenNameAnthony
person.givenNameCarmelo
person.identifier82868
person.identifier.ciencia-id8314-25F5-F0C8
person.identifier.ciencia-id1212-7478-6859
person.identifier.ciencia-id0C11-7785-3C4E
person.identifier.ciencia-idAF17-D6D1-3DC0
person.identifier.orcid0000-0003-2151-5444
person.identifier.orcid0000-0001-5458-4978
person.identifier.orcid0000-0003-4604-0780
person.identifier.orcid0000-0003-2239-2976
person.identifier.orcid0000-0002-7153-841X
person.identifier.orcid0000-0002-0924-5341
person.identifier.ridA-5627-2013
person.identifier.ridB-2939-2008
person.identifier.ridW-5458-2018
person.identifier.ridJ-2357-2014
person.identifier.scopus-author-id57076160300
person.identifier.scopus-author-id35081186500
person.identifier.scopus-author-id56379803400
person.identifier.scopus-author-id26538841000
person.identifier.scopus-author-id6603086941
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication27d5c2b7-5c5e-4dfc-a303-e77da5b0af2b
relation.isAuthorOfPublication157ced51-ac3c-4571-babe-0b4507ee5047
relation.isAuthorOfPublicationf9bcd5c9-9844-42d9-a2a5-adb177d0f9c9
relation.isAuthorOfPublicationc5f9be5f-ca94-40bc-b847-5ccb4a5ae6bd
relation.isAuthorOfPublication505f5ad4-0f53-4001-8a99-e85dc5bee346
relation.isAuthorOfPublicationccb986d5-6785-4343-a0d2-bd893ee91706
relation.isAuthorOfPublication.latestForDiscovery157ced51-ac3c-4571-babe-0b4507ee5047

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
27236156.pdf
Size:
1.79 MB
Format:
Adobe Portable Document Format