A summary of three case studies based on microarray data analysis is presented in this poster: the application of supervised clustering techniques to gene-expression data, the application of unsupervised clustering techniques to gene-expression data, and methods for comparing different clustering algorithms.

The yeast expression database we used are the results of De Risi, et.al. Science, 278, 680-686 (1997). In this study, two yeast cell populations were studied, one growing anaerobically (reference), the other having undergone a diauxic shift, from an anaerobic to an aerobic metabolism.
The object of the study was to assay for changes in gene expression over the time course of the diauxic shift and to correlate the gene subsets whose synthesis was induced or repressed with the known enzyme metabolic pathways involved in the diauxic shift.

Distinguishing Exon from Intron DNA sequences is one of the goals of the Human Genome Project. We have encoded and classified human sequences from the Fickett dataset analytically and visually.
Several visualization and data mining techniques were used to validate and attempt to discover new methods for distinguishing coding DNA sequences, or exons, from non-coding DNA sequences, or introns.

John Weinstein's group at NCI has carried out an analysis of the GI50 (concentration at which 50% growth inhibition is observed) values of 141 chemical agents of known Mechanism Of Action (MOA) screened individually on 60 cells lines. GI50 values were used to classify the 141 chemicals into their appropriate MOA by applying a neural net classifier, achieving a 91.5% accuracy. We demonstrate similar neural net classificaiton accuracy using the GI50 data, as well as clustering of 6 MOA groups using GI50 and chemical property descriptor databases.