See related Hot Paper, "The Rise of Free, Global Gene Expression Data Sets".
The rapid-fire advances in molecular biology, genetics, automation, and microarray analysis are a constant boon to drug discovery and basic biology, but that influx of data is also creating a serious quandary: How does one analyze it all? There is no shortage of approaches. As data piles up, computer scientists and statisticians step in to develop new methodologies. The problem is, there are too many options: "Biologists all of a sudden got totally lost in the choices of different data analysis methods," says Simon Lin, manager of Duke University's Bioinformatics Shared Resource in Durham, NC.
To combat that problem, Lin helped found an annual competition called Critical Assessment of Microarray Data Analysis (CAMDA; www.camda.duke.edu). The idea is to present statisticians with published data sets and turn them loose to analyze the data...
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