Novel H/ACA Box snoRNA Mining and Secondary Structure Prediction Algorithms

摘要

In this paper we propose a novel H/ACA box snoRNA gene mining algorithm, which is based on ensemble learning and a special secondary structure prediction algorithm. Three contributions arc made to improve current mining methods, including enriching the negative training set, using the ensemble classifiers for the class imbalance data, and developing a special secondary structure prediction algorithm for extracting features with high quality. The performance of learning method is proved by cross validation and the mining method is proved by the experiments on genome data.