Extracting gene regulation information for cancer classification
Pattern Recognition
Cancer classification by gradient LDA technique using microarray gene expression data
Data & Knowledge Engineering
Deriving meaningful rules from gene expression data for classification
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Gene Expression Data Classification Using Independent Variable Group Analysis
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
An expert system to classify microarray gene expression data using gene selection by decision tree
Expert Systems with Applications: An International Journal
Gene boosting for cancer classification based on gene expression profiles
Pattern Recognition
Definition of Valid Proteomic Biomarkers: A Bayesian Solution
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Inferring Meta-covariates in Classification
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Expectation Propagation for microarray data classification
Pattern Recognition Letters
Gene expression data classification using locally linear discriminant embedding
Computers in Biology and Medicine
Recursive Mahalanobis Separability Measure for Gene Subset Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A study of variable selection using g-prior distribution with ridge parameter
Computational Statistics & Data Analysis
Computers in Biology and Medicine
| Hi-index | 3.84 |
Summary: The fundamental problem of gene selection via cDNA data is to identify which genes are differentially expressed across different kinds of tissue samples (e.g. normal and cancer). cDNA data contain large number of variables (genes) and usually the sample size is relatively small so the selection process can be unstable. Therefore, models which incorporate sparsity in terms of variables (genes) are desirable for this kind of problem. This paper proposes a two-level hierarchical Bayesian model for variable selection which assumes a prior that favors sparseness. We adopt a Markov chain Monte Carlo (MCMC) based computation technique to simulate the parameters from the posteriors. The method is applied to leukemia data from a previous study and a published dataset on breast cancer. Supplementary information: http://stat.tamu.edu/people/faculty/bmallick.html