Abstract

DNA microarrays provide the technology needed to study gene expression. This technology facilitates large-scale surveys of gene expression in which transcript levels can be determined for thousands of genes simultaneously. These experiments generate an immense quantity of data. Investigators need computational methods to analyze this data to gain an understanding of the phenomena the data represents. This chapter presents two advanced methods for analyzing gene expression data that go beyond standard techniques but require the use of parallel computing. The first method provides for the assessment of codetermination of gene transcriptional states from large-scale simultaneous gene expression mesaurements with cDNA microarrays. The parallel implementation exploits the inherent parallelism exibited in the codetermination methodology that we apply. The second method involves classification using cDNA microarrays. The goal is to perform classification based on different expression patterns such as cancer classification. We present an efficient parallel implementation of the classifier where the computational work is distributed among available system processors.



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