Predicting cassette exons using transductive learning approaches

Recent advances in biotechnology have resulted in large volumes of genomic and proteomic data leading to the emergence of numerous in silico methods for annotation, such as supervised machine learning approaches. Such algorithms, however, require large amounts of labeled data for training. In practice, labeled data is oftentimes limited because it is difficult to obtain. Therefore, semi-supervised machine learning is preferable, in which classifiers trained on limited amounts of labeled data can be improved by exploiting the large amounts of unlabeled data. In this work, we focus on transductive learning, a special case of semi-supervised learning. A semi-supervised algorithm builds an inductive model that generalizes well to new, unseen (test) instances. In contrast, during the training phase, a transductive algorithm has access to the (test) instances that need to be classified, allowing advantageous utilization of these points in order to reach the best separation function. Compared to learning a classifier for use with future data, cassette exon identification is a suitable application for transductive learning, since the goal is to annotate a sequenced genome for which a limited amount of labeled data is available. We study the applicability of three popular transductive techniques and their compatibility with various kernels to the binary DNA classification problem of cassette exon identification. The results of our experiments suggest that transductive learning is a useful approach for assisting genome annotation.