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LIFT : lncRNA identification and function-prediction tool
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Deshpande, Sumukh, Shuttleworth, James, Yang, Jianhua, Taramonli, Sandy and England, Matthew (2021) LIFT : lncRNA identification and function-prediction tool. International Journal of Bioinformatics Research and Applications, 17 (6). pp. 512-536. doi:10.1504/IJBRA.2021.120535 ISSN 1744-5485.
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Official URL: http://dx.doi.org/10.1504/IJBRA.2021.120535
Abstract
Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a significant role in several biological processes. Accurate identification and sub-classification of lncRNAs is crucial for exploring their characteristic functions in the genome as most coding potential computation (CPC) tools fail to accurately identify, classify and predict their biological functions in plant species. In this study, a novel computational framework called LncRNA identification and function prediction tool (LIFT) has been developed, which implements least absolute shrinkage and selection operator (LASSO) optimisation and iterative random forests classification for selection of optimal features, a novel position-based classification (PBC) method for sub-classifying lncRNAs into different classes, and a Bayesian-based function prediction approach for annotating lncRNA transcripts. Using LASSO, LIFT selected 31 optimal features and achieved a 15-30% improvement in the prediction accuracy on plant species when evaluated against state-of-the-art CPC tools. Using PBC, LIFT successfully identified the intergenic and antisense transcripts with greater accuracy in the A. thaliana and Z. mays datasets.
Item Type: | Journal Article | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Journal or Publication Title: | International Journal of Bioinformatics Research and Applications | ||||||
Publisher: | Inderscience | ||||||
ISSN: | 1744-5485 | ||||||
Official Date: | 1 January 2021 | ||||||
Dates: |
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Volume: | 17 | ||||||
Number: | 6 | ||||||
Page Range: | pp. 512-536 | ||||||
DOI: | 10.1504/IJBRA.2021.120535 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Copyright Holders: | Inderscience Enterprises Ltd. |
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