Improving tRNAscan-SE Annotation Results via Ensemble Classifiers.

@article{Zou2015ImprovingTA,
  title={Improving tRNAscan-SE Annotation Results via Ensemble Classifiers.},
  author={Quan Zou and Jiasheng Guo and Yakun Ju and Meihong Wu and Xiangxiang Zeng and Zhiling Hong},
  journal={Molecular informatics},
  year={2015},
  volume={34 11-12},
  pages={761-70}
}
tRNAScan-SE is a tRNA detection program that is widely used for tRNA annotation; however, the false positive rate of tRNAScan-SE is unacceptable for large sequences. Here, we used a machine learning method to try to improve the tRNAScan-SE results. A new predictor, tRNA-Predict, was designed. We obtained real and pseudo-tRNA sequences as training data sets using tRNAScan-SE and constructed three different tRNA feature sets. We then set up an ensemble classifier, LibMutil, to predict tRNAs from… CONTINUE READING
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