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Antibody selection strategies and their impact in predicting clinical malaria based on multi-sera data

2024-01-25Journal article Open access
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dc.contributor.authorFonseca, André
dc.contributor.authorSpytek, Mikolaj
dc.contributor.authorBiecek, Przemysław
dc.contributor.authorCordeiro, Clara
dc.contributor.authorSepúlveda, Nuno
dc.date.accessioned2024-02-08T10:32:26Z
dc.date.available2024-02-08T10:32:26Z
dc.date.issued2024-01-25
dc.date.updated2024-02-01T04:30:10Z
dc.description.abstractNowadays, the chance of discovering the best antibody candidates for predicting clinical malaria has notably increased due to the availability of multi-sera data. The analysis of these data is typically divided into a feature selection phase followed by a predictive one where several models are constructed for predicting the outcome of interest. A key question in the analysis is to determine which antibodies should be included in the predictive stage and whether they should be included in the original or a transformed scale (i.e. binary/dichotomized).pt_PT
dc.description.sponsorshipGrant ref.: PPN/ULM/2020/1/00069/U/00001pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1186/s13040-024-00354-4pt_PT
dc.identifier.eissn1756-0381
dc.identifier.urihttp://hdl.handle.net/10400.1/20396
dc.language.isoengpt_PT
dc.language.rfc3066en
dc.peerreviewedyespt_PT
dc.publisherBMCpt_PT
dc.relationAnalysis of high-throughput antibody data for better understanding of immunogenetics and epidemiology of malaria
dc.relationCentre of Statistics and its Applications
dc.rights.holderThe Author(s)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectMultivariate serological datapt_PT
dc.subjectSuper learnerpt_PT
dc.subjectStatistical modellingpt_PT
dc.subjectMalaria outcome predictionpt_PT
dc.subjectRandom forestpt_PT
dc.titleAntibody selection strategies and their impact in predicting clinical malaria based on multi-sera datapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAnalysis of high-throughput antibody data for better understanding of immunogenetics and epidemiology of malaria
oaire.awardTitleCentre of Statistics and its Applications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/SFRH%2FBD%2F147629%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00006%2F2020/PT
oaire.citation.issue1pt_PT
oaire.citation.startPage2pt_PT
oaire.citation.titleBioData Miningpt_PT
oaire.citation.volume17pt_PT
oaire.fundingStreamOE
oaire.fundingStream6817 - DCRRNI ID
person.familyNameFonseca
person.familyNameHenrique Cordeiro
person.givenNameAndré Filipe Afonso de Sousa
person.givenNameClara Maria
person.identifier.ciencia-id0B1D-8695-6ADB
person.identifier.ciencia-idC71E-21A1-E882
person.identifier.orcid0000-0002-1026-6078
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationc242bab5-7d06-4b31-9fae-98447f5e2b96
relation.isAuthorOfPublication7e0bbaff-c584-461a-84e2-54f2a6c15876
relation.isAuthorOfPublication.latestForDiscoveryc242bab5-7d06-4b31-9fae-98447f5e2b96
relation.isProjectOfPublication7187315f-98e6-4671-9731-fc2bea229681
relation.isProjectOfPublication100e6d17-2f76-4282-b864-d0f887b34243
relation.isProjectOfPublication.latestForDiscovery100e6d17-2f76-4282-b864-d0f887b34243

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