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A novel riboswitch classification based on imbalanced sequences achieved by machine learning

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  • Solomon Shiferaw Beyene
  • Tianyi Ling
  • Blagoj Ristevski
  • Ming Chen

Abstract

Riboswitch, a part of regulatory mRNA (50–250nt in length), has two main classes: aptamer and expression platform. One of the main challenges raised during the classification of riboswitch is imbalanced data. That is a circumstance in which the records of a sequences of one group are very small compared to the others. Such circumstances lead classifier to ignore minority group and emphasize on majority ones, which results in a skewed classification. We considered sixteen riboswitch families, to be in accord with recent riboswitch classification work, that contain imbalanced sequences. The sequences were split into training and test set using a newly developed pipeline. From 5460 k-mers (k value 1 to 6) produced, 156 features were calculated based on CfsSubsetEval and BestFirst function found in WEKA 3.8. Statistically tested result was significantly difference between balanced and imbalanced sequences (p

Suggested Citation

  • Solomon Shiferaw Beyene & Tianyi Ling & Blagoj Ristevski & Ming Chen, 2020. "A novel riboswitch classification based on imbalanced sequences achieved by machine learning," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-23, July.
    • Handle: RePEc:plo:pcbi00:1007760
    DOI: 10.1371/journal.pcbi.1007760
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