Author(s): Fonseca, André ; Spytek, Mikolaj ; Biecek, Przemysław ; Cordeiro, Clara ; Sepúlveda, Nuno
Date: 2024
Persistent ID: http://hdl.handle.net/10400.1/20396
Origin: Sapientia - Universidade do Algarve
Subject(s): Multivariate serological data; Super learner; Statistical modelling; Malaria outcome prediction; Random forest
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).
