Incremental – Adaptive – Knowledge Based – Learning for Informative Rules Extraction in Classification Analysis of aGvHD

Acute graft-versus-host disease (aGvHD) is a serious systemic complication of allogeneic hematopoietic stem cell transplantation (HSCT) that occurs when alloreactive donor-derived T cells recognize host-recipient antigens as foreign. The early events leading to GvHD seem to occur very soon, presumably within hours from the graft infusion. Therefore, when the first signs of aGvHD clinically manifest, the disease has been ongoing for several days at the cellular level, and the inflammatory cytokine cascade is fully activated. So, it comes as no surprise that to identify biomarker signatures for approaching this very complex task is a critical issue. In the past, we have already approached it through joint molecular and computational analyses of gene expression data proposing a computational framework for this disease. Notwithstanding this, there aren’t in literature quantitative measurements able to identify patterns or rules from these biomarkers or from aGvHD data, thus this is the first work about the issue. In this paper first we have applied different feature selection techniques, combined with different classifiers to detect the aGvHD at onset of clinical signs, then we have focused on the aGvHD scenario and in the knowledge discovery issue of the classification techniques used in the computational framework.