Publicação
Antibody selection strategies and their impact in predicting clinical malaria based on multi-sera data
| Resumo: | Nowadays, 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). |
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| Autores principais: | Fonseca, André |
| Outros Autores: | Spytek, Mikolaj; Biecek, Przemysław; Cordeiro, Clara; Sepúlveda, Nuno |
| Assunto: | Multivariate serological data Super learner Statistical modelling Malaria outcome prediction Random forest |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Algarve |
| Idioma: | inglês |
| Origem: | Sapientia - Universidade do Algarve |
| Resumo: | Nowadays, 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). |
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