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Over the past decades, statisticians and machine-learning researchers havedeveloped literally thousands of new tools for the reduction ofhigh-dimensional data in order to identify the variables most responsible for aparticular trait. These tools have applications in a plethora of settings,including data analysis in the fields of business, education, forensics, andbiology (such as microarray, proteomics, brain imaging), to name a few.  In the present work, we focus our investigation on the limitations andpotential misuses of certain tools in the analysis of the benchmark coloncancer data (2,000 variables; Alon et al., 1999) and the prostate cancer data(6,033 variables; Efron, 2010, 2008). Our analysis demonstrates that modelsthat produce 100% accuracy measures often select different sets of genes andcannot stand the scrutiny of parameter estimates and model stability.  Furthermore, we created a host of simulation datasets and "artificialdiseases" to evaluate the reliability of commonly used statistical and datamining tools. We found that certain widely used models can classify the datawith 100% accuracy without using any of the variables responsible for thedisease. With moderate sample size and suitable pre-screening, stochasticgradient boosting will be shown to be a superior model for gene selection andvariable screening from high-dimensional datasets.

Finding Important Genes from High-Dimensional Data: An Appraisal of Statistical Tests and Machine-Learning Approaches