July 3rd 2008: Sounak Chakraborty : Gene Expression-Based Glioma Classification Using Hierarchical Bayesian Kernel Machine Mode

I have a pleasure to announce a guest lecture in Bioinformatics.
Everybody is welcome!

Thursday, July 3rd, at 3:15pm, Liivi 2-317 (Inst. of Computer Science)

Title: Gene Expression-Based Glioma Classification Using Hierarchical
Bayesian Kernel Machine Models


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.



Sounak Chakraborty
Assistant Professor
209F Middlebush Hall
Department of Statistics
University of Missouri-Columbia
Phone: (573) 882-3916
Fax: (573) 884-5524