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

André Fonseca, Mikolaj Spytek, Przemyslaw Biecek, Clara Cordeiro, Nuno Sepúlveda

Abstract: 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).

External IDs:dblp:journals/biodatamining/FonsecaSBCS24