Splice form prediction using Machine Learning

Accurate ab initio gene finding is still a major challenge in computational biology. We employ state-of-the-art machine learning techniques based on Hidden Semi-Markov-SVMs to assay and improve the accuracy of genome annotations. We applied our system, called mSplicer, on the Caenorhabditis elegans genome and were able to drastically improve its annotation.