Signal Processing - Special issue: Genomic signal processing
Gene prediction using multinomial probit regression with Bayesian gene selection
EURASIP Journal on Applied Signal Processing
Guest editorial: research on machine learning issues in biomedical informatics modeling
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Cancer classification by gradient LDA technique using microarray gene expression data
Data & Knowledge Engineering
Ensemble Neural Networks with Novel Gene-Subsets for Multiclass Cancer Classification
Neural Information Processing
Computer Methods and Programs in Biomedicine
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
CliDaPa: A new approach to combining clinical data with DNA microarrays
Intelligent Data Analysis - Knowledge Discovery in Bioinformatics
Expectation Propagation for microarray data classification
Pattern Recognition Letters
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Multiclass Kernel-Imbedded Gaussian Processes for Microarray Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An efficient statistical feature selection approach for classification of gene expression data
Journal of Biomedical Informatics
An optimally weighted fuzzy k-NN algorithm
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Cross-validation prior choice in Bayesian probit regression with many covariates
Statistics and Computing
Classification of gene expression data using Spiking Wavelet Radial Basis Neural Network
Expert Systems with Applications: An International Journal
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In microarray-based cancer classification and prediction, gene selection is an important research problem owing to the large number of genes and the small number of experimental conditions. In this paper, we propose a Bayesian approach to gene selection and classification using the logistic regression model. The basic idea of our approach is in conjunction with a logistic regression model to relate the gene expression with the class labels. We use Gibbs sampling and Markov chain Monte Carlo (MCMC) methods to discover important genes. To implement Gibbs Sampler and MCMC search, we derive a posterior distribution of selected genes given the observed data. After the important genes are identified, the same logistic regression model is then used for cancer classification and prediction. Issues for efficient implementation for the proposed method are discussed. The proposed method is evaluated against several large microarray data sets, including hereditary breast cancer, small round blue-cell tumors, and acute leukemia. The results show that the method can effectively identify important genes consistent with the known biological findings while the accuracy of the classification is also high. Finally, the robustness and sensitivity properties of the proposed method are also investigated.