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. 2018 May 15;8:7560. doi: 10.1038/s41598-018-25578-3

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Principal Component Analysis performed on training set compressed over 37% of variance in first two components. It revealed separate grouping of mirtrons and canonical microRNAs and some hidden relations between variables and miRNA classes. In general, the features that contribute most to class distinction, are the ones whose vectors point in the direction of a particular group of molecules. Here those most important features are: cytosine in 3p arm (mature3p_C), guanine composition of 5p arm (mature5p_G), cytosine composition of interarm region (interarm_C), adenine composition of 3p arm (mature3p_A) and length of precursor (hairpin_length).