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A complex eukaryotic genome contains several hundred to thousands of transcriptional and posttranscriptional regulators, which together make it possible to encode specific patterns of gene expression for many different conditions (1). The cooperation between transcription factors (TFs) and microRNAs (miRNAs) has been a particularly interesting topic, because they can regulate each other and define molecular network motifs with quantitative properties that either regulatory process alone cannot easily achieve (2, 3). Consequently, TF–miRNA interactions have been found to play important roles in biomedically relevant processes ranging from cancer to stem cell differentiation (4, 5). Computational approaches are increasingly used to integrate data interrogating different layers of gene regulation and successfully predict the specific context under which regulatory factors play important roles. In PNAS, Schulz et al. provide a compelling example of how machine learning is successfully applied on expression and regulatory sequence data to identify new roles for miRNAs in lung development and related diseases such as pulmonary fibrosis (6). Machine learning techniques, in particular classification, have a long and strong history in computational biology. Many genomewide inference problems related to molecular sequence and functional analysis can be phrased as classification problems. To this end, each sample is represented by a set of features (such as matches to a set of TF binding sites or simply all DNA/RNA substrings of a certain length). Sometimes, the classification result alone is of interest [such as when annotating protein domains or protein function (7)]; in more complex scenarios, classifiers are used in the context of a larger system [such as splice site recognizers in a model that annotates complex transcript structures (8)]. Genomewide data are inherently noisy, and our knowledge of the underlying biological processes is incomplete. This suggests the use of approaches that are able to …

Using machine learning to identify disease-relevant regulatory RNAs