Blood infection is highly prevalent in critical ill patients and can lead to sepsis and often death. It can be caused by bacteria or fungi and for appropriate treatment it is mandatory to identify the type of infection early. To find discriminating biomarkers, in situ high throughput gene expression profiling of immune cells after fungal or bacterial infection have been performed. However, these studies showed very heterogeneous results. To find a generic gene signature with discriminative power across all datasets, we implemented linear SVMs basing on Mixed Integer Linear Programming. We combined classifiers constraining them to use the same set of features. Learning with one pair of datasets and applying to the rest of the datasets showed 43?mprovement in consistency of the selected features (genes) while non-decreased classification performance (accuracy: 0.96). The final biomarkers comprised of 19 genes mostly involved in ERK-MAPK signalling being central in immune response.