Using Global Sequence Similarity to Enhance Biological Sequence Labeling

Identifying functionally important sites from biological sequences, formulated as a biological sequence labeling problem, has broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. In this paper, we present an approach to biological sequence labeling that takes into account the global similarity between biological sequences. Our approach combines unsupervised and supervised learning techniques. Given a set of sequences and a similarity measure defined on pairs of sequences, we learn a mixture of experts model by using spectral clustering to learn the hierarchical structure of the model and by using bayesian approaches to combine the predictions of the experts. We evaluate our approach on two important biological sequence labeling problems: RNA-protein and DNA-protein interface prediction problems. The results of our experiments show that global sequence similarity can be exploited to improve the performance of classifiers trained to label biological sequence data.