Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/6655
Title: On the performance of pre-microRNA detection algorithms
Authors: Saçar Demirci, Müşerref Duygu
Baumbach, Jan
Allmer, Jens
Keywords: MicroRNAs
RNA precursor
Gene expression regulation
Machine learning
Computational biology
Issue Date: Dec-2017
Publisher: Nature Publishing Group
Source: Saçar Demirci, M. D., Baumbach, J., and Allmer, J. (2017). On the performance of pre-microRNA detection algorithms. Nature Communications, 8(1). doi:10.1038/s41467-017-00403-z
Abstract: MicroRNAs are crucial for post-transcriptional gene regulation, and their dysregulation has been associated with diseases like cancer and, therefore, their analysis has become popular. The experimental discovery of miRNAs is cumbersome and, thus, many computational tools have been proposed. Here we assess 13 ab initio pre-miRNA detection approaches using all relevant, published, and novel data sets while judging algorithm performance based on ten intrinsic performance measures. We present an extensible framework, izMiR, which allows for the unbiased comparison of existing algorithms, adding new ones, and combining multiple approaches into ensemble methods. In an exhaustive attempt, we condense the results of millions of computations and show that no method is clearly superior; however, we provide a guideline for biomedical researchers to select a tool. Finally, we demonstrate that combining all of the methods into one ensemble approach, for the first time, allows reliable purely computational pre-miRNA detection in large eukaryotic genomes.
URI: http://doi.org/10.1038/s41467-017-00403-z
http://hdl.handle.net/11147/6655
ISSN: 2041-1723
Appears in Collections:Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File Description SizeFormat 
6655.pdfMakale682.52 kBAdobe PDFThumbnail
View/Open
Show full item record

CORE Recommender

SCOPUSTM   
Citations

25
checked on Feb 4, 2023

WEB OF SCIENCETM
Citations

28
checked on Dec 24, 2022

Page view(s)

112
checked on Jan 30, 2023

Download(s)

114
checked on Jan 30, 2023

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.