Data cleansing for computer models: a case study from immunology

Abstract

Knowledge discovery from databases (KDD) in biology largely depends on the use of accurate computer models of biological processes. KDD applications in immunology include the discovery of vaccine targets and new functional relations within the immune system. We describe a process of development and refinement of artificial neural network models of the human HLA-DR1 molecule, useful for the discovery of peptide vaccines. High accuracy of these models was achieved by data cleansing techniques and by cyclical retraining using new data.