Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques

Abstract

Although contemporary high-throughput –omics methods produce high-dimensional data, the resulting wealth of information is difficult to assess using traditional statistical procedures. Machine learning methods facilitate the detection of additional patterns, beyond the mere identification of lists of features that differ between groups.

Here, we demonstrate the utility of (1) supervised classification algorithms in class validation, and (2) unsupervised clustering in class discovery. We use data from our previous work that described the transcriptional profiles of gingival tissue samples obtained from subjects suffering from chronic or aggressive periodontitis (1) to test whether the two diagnostic entities were also characterized by differences on the molecular level, and (2) to search for a novel, alternative classification of periodontitis based on the tissue transcriptomes.

Using machine learning technology, we provide evidence for diagnostic imprecision in the currently accepted classification of periodontitis, and demonstrate that a novel, alternative classification based on differences in gingival tissue transcriptomes is feasible. The outlined procedures allow for the unbiased interrogation of high-dimensional datasets for characteristic underlying classes, and are applicable to a broad range of –omics data.