Publications

DNA Microarrays have revolutionized molecular biology, allowing simultaneous analysis of many thousands of genes, leading to many important applications, including creating novel, genetic-based diagnostic tests and finding new molecular targets for therapy. Because of the large number of genes and a typically small number of samples, analyzing DNA microarray data presents unique challenges.

I recently developed Clementine Application Templates for Microarray Data Analysis and described the results in a Webinar, "Analyzing Microarray Gene Expression Data" together with Sridhar Ramaswamy, M.D., a biologist at the Whitehead / MIT Center for Genome Research and Harvard Medical School.

The webinar covered the best practices for data mining of microarray gene expression data, including

• Preprocessing and filtering of microarray data
• Selecting the best set of genes for classification and clustering
• Reducing the false positives using randomization techniques
• Building classification and clustering models

Comments are welcome.