The nature of statistical learning theory
The nature of statistical learning theory
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Gene selection from microarray data for cancer classification-a machine learning approach
Computational Biology and Chemistry
Guest Editorial: Intelligent data analysis in biomedicine
Journal of Biomedical Informatics
Journal of Biomedical Informatics
Exploratory Consensus of Hierarchical Clusterings for Melanoma and Breast Cancer
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Ensemble gene selection for cancer classification
Pattern Recognition
Colon cancer prediction with genetics profiles using evolutionary techniques
Expert Systems with Applications: An International Journal
A two step method to identify clinical outcome relevant genes with microarray data
Journal of Biomedical Informatics
Journal of Biomedical Informatics
A heuristic biomarker selection approach based on professional tennis player ranking strategy
Computer Methods and Programs in Biomedicine
International Journal of Data Mining and Bioinformatics
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Discovery of differentially expressed genes between normal and diseased patients is a central research problem in bioinformatics. It is specially important to find few genetic markers which can be explored for diagnostic purposes. The performance of a set of markers is often measured by the associated classification accuracy. This motivates our ranking of genes depending on the minimum probability of classification errors (MPE) for each gene. In this work, we use Bayesian decision-making algorithm to compute MPE. A quantile-based probability density estimation technique is used for generating probability density functions of genes. The method is tested on three datasets: colon cancer, leukaemia, and hereditary breast cancer. The quality of the selected markers is evaluated by the classification accuracy obtained using support-vector-machine and a modified naive Bayes classifier. We obtain 96.77% accuracy in colon cancer and 97.06% accuracy in leukaemia, using only five genes in each case. Finally, using just three genes we get 100% accuracy in hereditary breast cancer. We also compare our results with those using the genes ranked by p-value and show that the genes ranked by MPE perform better or equal to those ranked by p-value.