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Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes.

07:00 EST 17th January 2019 | BioPortfolio

Summary of "Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes."

Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.

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This article was published in the following journal.

Name: Briefings in bioinformatics
ISSN: 1477-4054
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Bioinformatics is the application of computer software and hardware to the management of biological data to create useful information. Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied...


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