Statistical analysis with missing data
Statistical analysis with missing data
The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Integration of gene signatures using biological knowledge
Artificial Intelligence in Medicine
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Objective: The problem of gene selection has been extensively studied in a number of scientific works using various kinds of methods. However, the application of a linear neuron is a novel approach possessing several advantages. In this work we propose to study the behavior of such a linear neuron, appropriately adapted and trained to the problem of gene selection in the DNA microarray experiment. Methods and materials: We explore the proposed approach in terms of an accuracy evaluation criterion, which is used to assess the performance of the proposed methodology, but we also evaluate the produced results in terms of cluster quality and survival prediction. Cluster quality reflects the ability of the method to select differentially expressed genes, which in turn leads to better clustering and survival prediction. Results: We directly compare the proposed methodology with RFE-SVM, a well known and broadly accepted method demonstrating remarkable performance on various data sets of clinical interest. Conclusions: Conducted computational experiments show that the proposed approach can be efficiently used within the field of gene selection producing high-quality results in terms of accuracy and robustness.