Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs

Ma, Y; Yu, Z; Han, G; Li, J; Anh, V

Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs

Ma, Y Yu, Z Han, G Li, J

Anh, V

Permalink

Publication Type:: Journal Article
Citation:: BMC Bioinformatics, 2018, 19
Issue Date:: 2018-12-31

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published VersionAdobe PDF (1.45 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Ma, Y	en_US
dc.contributor.author	Yu, Z	en_US
dc.contributor.author	Han, G	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0003-1833-7413	en_US
dc.contributor.author	Anh, V	en_US
dc.date.issued	2018-12-31	en_US
dc.identifier.citation	BMC Bioinformatics, 2018, 19	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132031
dc.description.abstract	© 2018 The Author(s). Background: Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. However, most of them mainly focus on secondary structure information of pre-microRNAs, while ignoring sequence-order information and sequence evolution information. Results: We use new features for the machine learning algorithms to improve the classification performance by characterizing both sequence order evolution information and secondary structure graphs. We developed three steps to extract these features of pre-microRNAs. We first extract features from PSI-BLAST profiles and Hilbert-Huang transforms, which contain rich sequence evolution information and sequence-order information respectively. We then obtain properties of small molecular networks of pre-microRNAs, which contain refined secondary structure information. These structural features are carefully generated so that they can depict both global and local characteristics of pre-microRNAs. In total, our feature space covers 591 features. The maximum relevance and minimum redundancy (mRMR) feature selection method is adopted before support vector machine (SVM) is applied as our classifier. The constructed classification model is named MicroRNA -NHPred. The performance of MicroRNA -NHPred is high and stable, which is better than that of those state-of-the-art methods, achieving an accuracy of up to 94.83% on same benchmark datasets. Conclusions: The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the sequences and secondary structures, which are capable of characterizing the sequence evolution information and sequence-order information, and global and local information of pre-microRNAs secondary structures. MicroRNA -NHPred is a valuable method for pre-microRNAs identification. The source codes of our method can be downloaded from https://github.com/myl446/MicroRNA-NHPred.	en_US
dc.relation.ispartof	BMC Bioinformatics	en_US
dc.relation.isbasedon	10.1186/s12859-018-2518-2	en_US
dc.subject.classification	Bioinformatics	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	MicroRNAs	en_US
dc.subject.mesh	Computational Biology	en_US
dc.subject.mesh	Genome, Human	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Machine Learning	en_US
dc.subject.mesh	Support Vector Machine	en_US
dc.title	Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs	en_US
dc.type	Journal Article
utslib.citation.volume	19	en_US
utslib.for	0807 Library and Information Studies	en_US
utslib.for	0604 Genetics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

© 2018 The Author(s). Background: Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. However, most of them mainly focus on secondary structure information of pre-microRNAs, while ignoring sequence-order information and sequence evolution information. Results: We use new features for the machine learning algorithms to improve the classification performance by characterizing both sequence order evolution information and secondary structure graphs. We developed three steps to extract these features of pre-microRNAs. We first extract features from PSI-BLAST profiles and Hilbert-Huang transforms, which contain rich sequence evolution information and sequence-order information respectively. We then obtain properties of small molecular networks of pre-microRNAs, which contain refined secondary structure information. These structural features are carefully generated so that they can depict both global and local characteristics of pre-microRNAs. In total, our feature space covers 591 features. The maximum relevance and minimum redundancy (mRMR) feature selection method is adopted before support vector machine (SVM) is applied as our classifier. The constructed classification model is named MicroRNA -NHPred. The performance of MicroRNA -NHPred is high and stable, which is better than that of those state-of-the-art methods, achieving an accuracy of up to 94.83% on same benchmark datasets. Conclusions: The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the sequences and secondary structures, which are capable of characterizing the sequence evolution information and sequence-order information, and global and local information of pre-microRNAs secondary structures. MicroRNA -NHPred is a valuable method for pre-microRNAs identification. The source codes of our method can be downloaded from https://github.com/myl446/MicroRNA-NHPred.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/132031