Information theoretic feature selection for high dimensional metagenomic data

Extremely high dimensional data sets are common in genomic classification scenarios, but they are particularly prevalent in metagenomic studies that represent samples as abundances of taxonomic units. Furthermore, the data dimensionality is typically much larger than the number of observations collected for each instance, a phenomenon known as curse of dimensionality, a particularly challenging problem for most machine learning algorithms. The biologists collecting and analyzing data need efficient methods to determine relationships between classes in a data set and the variables that are capable of differentiating between multiple groups in a study. The most common methods of metagenomic data analysis are those characterized by α- and β-diversity tests; however, neither of these tests allow scientists to identify the organisms that are most responsible for differentiating between different categories in a study. In this paper, we present an analysis of information theoretic feature selection methods for improving the classification accuracy with metagenomic data.