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

Solomon Shiferaw Beyene, Tianyi Ling, Blagoj Ristevski and Ming Chen

PLOS Computational Biology, 2020, vol. 16, issue 7, 1-23

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

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1007760

DOI: 10.1371/journal.pcbi.1007760

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