Abstract
The importance of RNA protein-coding gene regulation is by now well appreciated. Non-coding RNAs (ncRNAs) are known to regulate gene expression at practically every stage, ranging from chromatin packaging to mRNA translation. However the functional characterization of specific instances remains a challenging task in genome scale settings. For this reason, automatic annotation approaches are of interest. Existing computational methods are either efficient but non-accurate or they offer increased precision, but present scalability problems.
In this article, we present a predictive system based on kernel methods, a type of machine learning algorithm grounded in statistical learning theory. We employ a flexible graph encoding to preserve multiple structural hypotheses and exploit recent advances in representation and model induction to scale to large data volumes. Experimental results on tens of thousands of ncRNA sequences available from the Rfam database indicate that we can not only improve upon state-of-the-art predictors, but also achieve speedups of several orders of magnitude.
The code is available from http://www.bioinf.uni-freiburg.de/~costa/EDeN.tgz.
