Abstract
This paper presents a comprehensive study of gene expression patterns originating from a diffuse large B–cell lymphoma (DLBCL) database. It focuses on the implementation of feature selection and classification techniques. Thus, it firstly tackles the identification of relevant genes for the prediction of DLBCL types. It also allows the determination 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 DLBCL. This research suggests that the data may be insufficient to accurately 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.
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Aguilar-Ruiz, J.S., Azuaje, F., Riquelme, J.C. (2004). Data Mining Approaches to Diffuse Large B–Cell Lymphoma Gene Expression Data Interpretation. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2004. Lecture Notes in Computer Science, vol 3181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30076-2_28
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DOI: https://doi.org/10.1007/978-3-540-30076-2_28
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