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. 2018 Jul 31;20(6):2009–2027. doi: 10.1093/bib/bby065

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© The Author(s) 2018. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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Figure 1. — Framework of this study. Data sets used in our experiments are collected from GENCODE and Ensembl. Only one transcript from each gene is used. In addition to sequence-intrinsic composition, features are also extracted from multi-scale secondary structure and EIIP-based physicochemical property using two feature selection schemes. Evaluated with 10-fold CV and ROC curve, the optimal feature combination and machine learning algorithm are obtained to develop a new method for lncRNA identification. This method is benchmarked against five popular tools on five species, and it is finally included in LncFinder, which is a highly flexible package for lncRNA identification and analysis. LncFinder is published as R package as well as web server.