Specificity Enhancement in microRNA Target Prediction through Knowledge Discovery

Abstract

Machine learning is widely used in commercial businesses where vast amounts of data are produced. The life-sciences, molecular oriented research in particular, is a rapidly growing field which has gained a lot of attention lately especially now that the genomes of the major research model species have been sequenced and are publicly available. With the development of more and more large-scale and advanced techniques in biology, the need to discover hidden information triggered the application of machine learning in the field of the life-sciences. But these applications bear a risk, since, first of all, most biological mechanisms are not yet fully understood, and second, some techniques produce too little experimental data due to the limitations of these techniques, thereby making machine learning unreliable. In this chapter, we explained how we integrated different machine learning algorithms and tuned and optimized experimental setups to a growing but not yet mature research field, miRNA target prediction. The innovation of this approach is not only integration and optimization of machine learning algorithms, but also the prediction through new features in miRNA relationship instead of widely studied features of miRNA-target interaction. Existing methods for analysis have shown to be insufficient in identifying targets from this perspective. As illustrated in the methods and results sections, pattern recognition generates models enabling class descriptions. In this case, a rather high misclassification error around 30% is surfacing. In contrast, subgroup discovery aims at discovering statistically unusual patterns of interesting classes (Zelezny & Lavrac, 2006). It discovers three main groups describing only the positive miRNA pairs. One of the disadvantages of pattern recognition method is that the model is not biologically interpretable. Consisting of linear or quadratic transformations of features, the classifiers tell nothing about the mechanisms of miRNA-target binding. However decision tree an