Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Applying genetic algorithms and support vector machines to the gene selection problem
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - VIII Brazilian Symposium on Neural Networks
Linear Separability of Gene Expression Data Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Two-Step Cross-Entropy Feature Selection for Microarrays—Power Through Complementarity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Fusing biomedical multi-modal data for exploratory data analysis
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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Microarray experiments generate large datasets with expression values for thousands of genes, but not more than a few dozens of samples. A challenging task with these data is to reveal groups of genes which act together and whose collective expression is strongly associated with an outcome variable of interest. To find these groups, we suggest the use of supervised algorithms: these are procedures which use external information about the response variable for grouping the genes. We present Pelora, an algorithm based on penalized logistic regression analysis, that combines gene selection, gene grouping and sample classification in a supervised, simultaneous way. With an empirical study on six difterent microarray datasets, we show that Pelora identifies gene groups whose expression centroids have very good predictive potential and yield results that can keep up with state-of-the-art classification methods based on single genes. Thus, our gene groups can be beneficial in medical diagnostics and prognostics, but they may also provide more biological insights into gene function and regulation.