Learning Rough Set Classifiers from Gene Expressions and Clinical Data

Biological research is currently undergoing a revolution. With the advent of microarray technology the behavior of thousands of genes can be measured simultaneously. This capability opens a wide range of research opportunities in biology, but the technology generates a vast amount of data that cannot be handled manually. Computational analysis is thus a prerequisite for the success of this technology, and research and development of computational tools for microarray analysis are of great importance. One application of microarray technology is cancer studies where supervised learning may be used for predicting tumor subtypes and clinical parameters. We present a general Rough Set approach for classification of tumor samples analyzed with microarrays. This approach is tested on a data set of gastric tumors, and we develop classifiers for six clinical parameters. Also at Department of Computer and Information Science, Norwegian University of Science and Technology, N-7491 TRONDHEIM, NOR...

• Type: Journal
• Year: 2002
• Where: FUIN
• Authors: Herman Midelfart, Henryk Jan Komorowski, Kristin Nørsett, Fekadu Yadetie, Arne K. Sandvik, Astrid Lægreid
• Keywords: Clinical Parameters, FUIN 2002, Microarray Technology, Research