Affiliations: Department of Computer and Information Science,
Norwegian University of Science and Technology, N-7491 Trondheim, Norway | The Linnaeus Centre for Bioinformatics, Uppsala University, BMC Box 598, SE-751
24 Uppsala, Sweden | Department of Physiology and Biomedical Engineering,
Norwegian University of Science and Technology, N-7489 Trondheim, Norway
Abstract: 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. One major
obstacle in training classifiers from microarray data is that the number of
objects is much smaller that the number of attributes. We therefore introduce a
feature selection method based on bootstrapping for selecting genes that
discriminate significantly between the classes, and study the performance of
this method. Moreover, the efficacy of several learning and discretization
methods implemented in the ROSETTA system [18] is examined. Their performance
is compared to that of linear and quadratic discrimination analysis. The
classifiers are also biologically validated. One of the best classifiers is
selected for each clinical parameter, and the connection between the genes used
in these classifiers and the parameters are compared to the establish knowledge
in the biomedical literature.
