The nature of statistical learning theory
The nature of statistical learning theory
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Machine Learning
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Regularized simultaneous model selection in multiple quantiles regression
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
A Bayesian approach to model-based clustering for binary panel probit models
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
A two step method to identify clinical outcome relevant genes with microarray data
Journal of Biomedical Informatics
Functional gradient ascent for Probit regression
Pattern Recognition
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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With the arrival of gene expression microarrays a new challenge has opened up for identification or classification of cancer tissues. Due to the large number of genes providing valuable information simultaneously compared to very few available tissue samples the cancer staging or classification becomes very tricky. In this paper we introduce a hierarchical Bayesian probit model for two class cancer classification. Instead of assuming a linear structure for the function that relates the gene expressions with the cancer types we only assume that the relationship is explained by an unknown function which belongs to an abstract functional space like the reproducing kernel Hilbert space. Our formulation automatically reduces the dimension of the problem from the large number of covariates or genes to a small sample size. We incorporate a Bayesian gene selection scheme with the automatic dimension reduction to adaptively select important genes and classify cancer types under an unified model. Our model is highly flexible in terms of explaining the relationship between the cancer types and gene expression measurements and picking up the differentially expressed genes. The proposed model is successfully tested on three simulated data sets and three publicly available leukemia cancer, colon cancer, and prostate cancer real life data sets.