Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Defining the Importance Score of Human MicroRNAs and Their Single Nucleotide Mutants Using Random Forest Regression and Sequence Data

Defining the Importance Score of Human MicroRNAs and Their Single Nucleotide Mutants Using Random... To dig critical microRNAs (miRNAs) from the big miRNAome, it is becoming emergent to quantify the importance of miRNAs. However, computational methods for this purpose are still not available. Some sequence features associated with miRNA conservation are revealed and miRNA disease spectrum width (DSW) is defined as a score to measure the importance of miRNAs to some extent. Here, a random forest based regression model, microRNA importance calculator (MIC), is proposed to estimate the relationship between miRNA sequence and DSW score. The result shows that MIC score fits DSW score very well. Moreover, the MIC score significantly correlates with some established biological metrics for miRNA importance, such as miRNA conservation and expression level. Finally, MIC is extended to explore miRNAs with different importance scores across species for evaluating the possibility of druggable miRNAs and estimate how single nucleotide mutants affect the importance of miRNAs. MIC is available at http://www.cuilab.cn/mic. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advanced Theory and Simulations Wiley

Defining the Importance Score of Human MicroRNAs and Their Single Nucleotide Mutants Using Random Forest Regression and Sequence Data

Download PDF
8 pages

Loading next page...
 
/lp/wiley/defining-the-importance-score-of-human-micrornas-and-their-single-hAf4hGUOSi
Publisher
Wiley
Copyright
© 2019 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim
eISSN
2513-0390
DOI
10.1002/adts.201900083
Publisher site
See Article on Publisher Site

Abstract

To dig critical microRNAs (miRNAs) from the big miRNAome, it is becoming emergent to quantify the importance of miRNAs. However, computational methods for this purpose are still not available. Some sequence features associated with miRNA conservation are revealed and miRNA disease spectrum width (DSW) is defined as a score to measure the importance of miRNAs to some extent. Here, a random forest based regression model, microRNA importance calculator (MIC), is proposed to estimate the relationship between miRNA sequence and DSW score. The result shows that MIC score fits DSW score very well. Moreover, the MIC score significantly correlates with some established biological metrics for miRNA importance, such as miRNA conservation and expression level. Finally, MIC is extended to explore miRNAs with different importance scores across species for evaluating the possibility of druggable miRNAs and estimate how single nucleotide mutants affect the importance of miRNAs. MIC is available at http://www.cuilab.cn/mic.

Journal

Advanced Theory and Simulations – Wiley

Published: Sep 1, 2019

Keywords: ; ; ; ;

References