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SalivaPrint as a non-invasive diagnostic tool

2020-01-01Conference object Open access
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dc.contributor.authorEsteves, Eduardo
dc.contributor.authorCruz, Igor
dc.contributor.authorEsteves, Ana Cristina
dc.contributor.authorBarros, Marlene
dc.contributor.authorRosa, Nuno
dc.date.accessioned2021-03-20T17:05:25Z
dc.date.available2021-03-20T17:05:25Z
dc.date.issued2020-01-01
dc.description.abstractCurrently, the molecular diagnosis is based on the quantification of RNA, proteins and metabolites because they present changes in their quantity related to clinical situations. The same molecules are not generally suitable for early diagnosis or to follow clinical evolution, making necessary strategies to evaluate the complete molecular scenario. There are already experimental strategies that allow the determination of total protein profiles from saliva samples (the SalivaPrint). The goal of this work is to identify a profile of saliva proteins (similar to a fingerprint) and, using computational methods, identify how this profiles changes with age and gender. So far it has been possible to collect 79 samples as well as the metadata associated with each sample using an electronic questionnaire developed by us. A total protein profile was obtained and their association with gender was verified using statistical methods. Currently we are developing the Python scripts for automatic data acquiring and normalization. Total protein profiles annotation on a database (SalivaPrintDB) and their integration with the factors that affects them using machine learning strategies can empower the use of the approach proposed on this work as a tool for monitoring the individual's health status.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.5220/0009163506770682pt_PT
dc.identifier.eid85083711531
dc.identifier.isbn9789897583988
dc.identifier.urihttp://hdl.handle.net/10400.14/32301
dc.identifier.wos000571479400075
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectHealth diagnosticpt_PT
dc.subjectMachine learning strategiespt_PT
dc.subjectSaliva diagnosticpt_PT
dc.subjectSaliva protein profilept_PT
dc.titleSalivaPrint as a non-invasive diagnostic toolpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage682pt_PT
oaire.citation.startPage677pt_PT
oaire.citation.titleHEALTHINF 2020 - 13th International Conference on Health Informatics, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020pt_PT
person.familyNameEsteves
person.familyNameEsteves
person.familyNameBarros
person.familyNameRosa
person.givenNameEduardo
person.givenNameAna Cristina
person.givenNameMarlene
person.givenNameNuno
person.identifier82868
person.identifierAAG-6405-2021
person.identifier.ciencia-id8314-25F5-F0C8
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person.identifier.ciencia-id1212-7478-6859
person.identifier.orcid0000-0001-5458-4978
person.identifier.orcid0000-0003-2239-2976
person.identifier.orcid0000-0003-0631-4062
person.identifier.orcid0000-0003-4604-0780
person.identifier.ridB-2939-2008
person.identifier.ridA-5627-2013
person.identifier.scopus-author-id57076160300
person.identifier.scopus-author-id56379803400
person.identifier.scopus-author-id7102895790
person.identifier.scopus-author-id35081186500
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObject
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