Relevance Vector Machines for classifying points and regions in biological sequences

Abstract

The Relevance Vector Machine (RVM) is a recently developed machine learning framework capable of building simple models from large sets of candidate features. Here, we describe a protocol for using the RVM to explore very large numbers of candidate features, and a family of models which apply the power of the RVM to classifying and detecting interesting points and regions in biological sequence data. The models described here have been used successfully for predicting transcription start sites and other features in genome sequences.