Data Mining Approaches to Diffuse Large B–Cell Lymphoma Gene Expression Data Interpretation

Abstract. This paper presents a comprehensive study of gene expres-sion patterns originating from a diffuse large B–cell lymphoma (DLBCL) database. It focuses on the implementation of feature selection and clas-sification techniques. Thus, it firstly tackles the identification of relevant genes for the prediction of DLBCL types. It also allows the determina-tion of key biomarkers to differentiate two subtypes of DLBCL samples: Activated B–Like and Germinal Centre B–Like DLBCL. Decision trees provide knowledge–based models to predict types and subtypes of DL-BCL. This research suggests that the data may be insufficient to accu-rately predict DLBCL types or even detect functionally relevant genes. However, these methods represent reliable and understandable tools to start thinking about possible interesting non–linear interdependencies.