The nature of statistical learning theory
The nature of statistical learning theory
Class prediction and discovery using gene expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
SVM vs Regularized Least Squares Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Permutation tests for classification
COLT'05 Proceedings of the 18th annual conference on Learning Theory
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Artificial Intelligence in Medicine
Permutation Tests for Studying Classifier Performance
The Journal of Machine Learning Research
Artificial Intelligence in Medicine
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Motivations: One of the main problems in cancer diagnosis by using DNA microarray data is selecting genes relevant for the pathology by analyzing their expression profiles in tissues in two different phenotypical conditions. The question we pose is the following: how do we measure the relevance of a single gene in a given pathology? Methods: A gene is relevant for a particular disease if we are able to correctly predict the occurrence of the pathology in new patients on the basis of its expression level only. In other words, a gene is informative for the disease if its expression levels are useful for training a classifier able to generalize, that is, able to correctly predict the status of new patients. In this paper we present a selection bias free, statistically well founded method for finding relevant genes on the basis of their classification ability. Results: We applied the method on a colon cancer data set and produced a list of relevant genes, ranked on the basis of their prediction accuracy. We found, out of more than 6500 available genes, 54 overexpressed in normal tissues and 77 overexpressed in tumor tissues having prediction accuracy greater than 70% with p-value@?@?0.05. Conclusions: The relevance of the selected genes was assessed (a) statistically, evaluating the p-value of the estimate prediction accuracy of each gene; (b) biologically, confirming the involvement of many genes in generic carcinogenic processes and in particular for the colon; (c) comparatively, verifying the presence of these genes in other studies on the same data-set.