Multiclass Cancer Diagnosis Using Bayesian Kernel Machine Models

• Activity Number: 74
• Type: Topic Contributed
• Date/Time: Monday, August 9, 2004 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Bani K. Mallick*+
• Companies: Texas A&M University
• Keywords: microarrays ; cancer classification ; support vector machine ; Bayes classification ; MCMC

Abstract

Precise classification of tumors is critical for cancer diagnosis and treatment. Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. In recent years, several works showed successful classification of pairs of tumors types using gene expression patterns. However, the simultaneous classification across a heterogeneous set of tumor types has not been well-studied yet. Usually, this multicategory classification problems are solved by using a binary classifiers which may fail in a variety of circumstances. We tackle the problem of cancer classification in the context of multiple tumor type. We develop a full probabilistic model-based approach, specifically probabilistic relevance vector machine (RVM), as well as support vector machines for multicategory classification. We develop a hierarchical model where the unknown smoothing parameter is interpreted as a shrinkage parameter. We assign a prior distribution to it and obtain its posterior distribution via Bayesian computation. In this way, we not only obtain the point predictors but also the associated measures of uncertainty.