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Simplifying data analysis in biomedical research: an automated, user-friendly tool

2024-04Journal article Open access
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dc.contributor.authorAraújo, Rúben
dc.contributor.authorRamalhete, Luís
dc.contributor.authorViegas, Ana
dc.contributor.authorVon Rekowski, Cristiana P.
dc.contributor.authorFonseca, Tiago A.
dc.contributor.authorCalado, Cecília
dc.contributor.authorBento, Luís
dc.date.accessioned2024-06-28T11:03:44Z
dc.date.available2024-06-28T11:03:44Z
dc.date.issued2024-04
dc.description.abstractRobust data normalization and analysis are pivotal in biomedical research to ensure that observed differences in populations are directly attributable to the target variable, rather than disparities between control and study groups. ArsHive addresses this challenge using advanced algorithms to normalize populations (e.g., control and study groups) and perform statistical evaluations between demographic, clinical, and other variables within biomedical datasets, resulting in more balanced and unbiased analyses. The tool's functionality extends to comprehensive data reporting, which elucidates the effects of data processing while maintaining dataset integrity. Additionally, ArsHive is complemented by A.D.A. (Autonomous Digital Assistant), which employs OpenAI's GPT-4 model to assist researchers with inquiries, enhancing the decision-making process. In this proof-of-concept study, we tested ArsHive on three different datasets derived from proprietary data, demonstrating its effectiveness in managing complex clinical and therapeutic information and highlighting its versatility for diverse research fields.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAraújo R, Ramalhete L, Viegas A, Von Rekowski CP, Fonseca TA, Calado CR, et al. Simplifying data analysis in biomedical research: an automated, user-friendly tool. Methods Protoc. 2024;7(3):36.pt_PT
dc.identifier.doi10.3390/mps7030036pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.21/17547
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relation.publisherversionhttps://www.mdpi.com/2409-9279/7/3/36pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectLLM modelspt_PT
dc.subjectBiomedical researchpt_PT
dc.subjectHigh dimensional data analysispt_PT
dc.subjectMachine learningpt_PT
dc.titleSimplifying data analysis in biomedical research: an automated, user-friendly toolpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue3pt_PT
oaire.citation.startPage36pt_PT
oaire.citation.titleMethods and Protocolspt_PT
oaire.citation.volume7pt_PT
person.familyNameCalado
person.givenNameCecília
person.identifier130332
person.identifier.ciencia-id9418-E320-3177
person.identifier.orcid0000-0002-5264-9755
person.identifier.ridE-2102-2014
person.identifier.scopus-author-id6603163260
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicatione8577257-c64c-4481-9b2b-940fedb360cc
relation.isAuthorOfPublication.latestForDiscoverye8577257-c64c-4481-9b2b-940fedb360cc

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