Distinguishing Endogenous Retroviral LTRs from SINE Elements Using Features Extracted from Evolved Side Effect Machines

@article{Ashlock2012DistinguishingER,
  title={Distinguishing Endogenous Retroviral LTRs from SINE Elements Using Features Extracted from Evolved Side Effect Machines},
  author={Wendy Ashlock and Suprakash Datta},
  journal={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
  year={2012},
  volume={9},
  pages={1676-1689}
}
Side effect machines produce features for classifiers that distinguish different types of DNA sequences. They have the, as yet unexploited, potential to give insight into biological features of the sequences. We introduce several innovations to the production and use of side effect machine sequence features. We compare the results of using consensus sequences and genomic sequences for training classifiers and find that more accurate results can be obtained using genomic sequences. Surprisingly… CONTINUE READING
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