Seminar Notice
 
Statistical Laboratory
Iowa State University
 
DATE AND TIME:  Monday, October 24, 2005, 4:10 p.m.
 
PLACE: 319 Snedecor
 
SPEAKER: Sounak Chakraborty, Department of Statistics, University of Missouri, Columbia
 
TITLE:  Gene Expression Based Glioma Classification Using Hierarchical Bayesian Vector Machines
 
In modern clinical neuro-oncology, the diagnosis and classification of malignant gliomas remains problematic and effective therapies are still elusive. As patient prognosis and therapeutic decisions rely on accurate pathological grading or classification of tumor cells, extensive investigation is going on for accurately identifying the types of glioma cancer. Unfortunately, many malignant gliomas are diagnostically challenging; these non-classic lesions are difficult to classify by histological features, thereby resulting in considerable interobserver variability and limited diagnosis reproducibility. In recent years, there has been a move towards the use of cDNA microarrays for tumor classification. These high-throughput assays provide relative mRNA expression measurements simultaneously for thousands of genes. A key statistical task is to perform classification via different expression patterns. Gene expression profiles may offer more information than classical morphology and may provide a better alternative to the classical tumor diagnosis schemes. The classification becomes more difficult when there are more than two cancer types, as with glioma.
 
This paper considers several Bayesian classification methods for the analysis of the glioma cancer with microarray data based on reproducing kernel Hilbert space under the multiclass setup. We consider the multinomial logit likelihood as well as the likelihood related to the muliclass Support Vector Machine (SVM) model. It is shown that our proposed Bayesian classification models with multiple shrinkage parameters can produce much accurate classification scheme for the glioma cancer compared to the several existing classical methods. We have also proposed a Bayesian variable selection scheme for selecting the differentially expressed genes integrated with our model.This integrated approach improves classifier design by yielding simultaneous gene selection.
 
KEY WORDS: Gibbs sampling; Markov chain Monte Carlo; Metropolis-Hastings algorithm; microarrays; reproducing kernel Hilbert space; shrinkage parameters; support vector machines.
 
COFFEE:  3:45 p.m., 104 Snedecor Hall
 
Seminar schedules and abstracts are available via WWW:  http://www.stat.iastate.edu/

Jeanette La Grange
Department of Statistics
102 Snedecor
Iowa State University
Ames, IA  50010-1210
515 294-3440 (office)
515 294-4040 (fax)
http://www.stat.iastate.edu/directory/staff/jeanette.html