Using Supervised Complexity Measures in the Analysis of Cancer Gene Expression Data Sets
BSB '09 Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
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One of the main kinds of computational tasks regarding gene expression data is the construction of classifiers (models), often via some Machine Learning (ML) technique and given data sets, to automatically discriminate expression patterns from cancer (tumor) and normal tissues or from subtypes of cancers. A very distinctive characteristic of these data sets is its high dimensionality and the fewer number of data items. Such a characteristic makes the induction of accurate ML models difficult (e.g., it could lead to model overfitting). In this context, we present an empirical study on the complexity of the classification task of gene expression data sets, related to cancer, used for classification purposes. In order to do so, we measure the complexity of the ML models used to perform the tumors' classification. The results indicate that most of these data sets can be effectively discriminated by a simple linear function.