Fonseca, AndréSpytek, MikolajBiecek, PrzemysławCordeiro, ClaraSepúlveda, Nuno2024-02-082024-02-082024-01-25http://hdl.handle.net/10400.1/20396Nowadays, 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).engMultivariate serological dataSuper learnerStatistical modellingMalaria outcome predictionRandom forestjournal article2024-02-01The Author(s)10.1186/s13040-024-00354-41756-0381