Multicriteria Gene Screening for Microarray Experiments

Over the past decade there has been an explosion in the amount of genomic data available to biomedical researchers due to advances in biotechnology. For example, using gene microarrays, it is now possible to probe a person’s gene expression profile over the more than 20,000 genes in the human genome. Signals extracted from gene microarray experiments can be linked to genetic factors underlying disease, development, and aging in a population. This has greatly accelerated the pace of gene discovery. However, the massive scale and experimental variability of genomic data makes extraction of biologically significant genetic information very challenging. One of the most important problems is to select a list of genes which are both biologically and statistically significant based on the outcomes of gene microarray experiments. We will describe a novel multicriteria method that we have developed for this gene selection problem that allows tight control of both minimum observable differential change (biological significance) and familywise error rate (statistical significance) and also provides a set of simultaneous confidence intervals for the differences.

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