Please use this identifier to cite or link to this item:
Title: Improving the quality of positive datasets for the establishment of machine learning models for pre-microRNA detection
Authors: Saçar Demirci, Müşerref Duygu
Allmer, Jens
Keywords: MicroRNAs
Machine learning
High quality
Positive data
Issue Date: 2017
Publisher: Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.)
Abstract: MicroRNAs (miRNAs) are involved in the post-transcriptional regulation of protein abundance and thus have a great impact on the resulting phenotype. It is, therefore, no wonder that they have been implicated in many diseases ranging from virus infections to cancer. This impact on the phenotype leads to a great interest in establishing the miRNAs of an organism. Experimental methods are complicated which led to the development of computational methods for pre-miRNA detection. Such methods generally employ machine learning to establish models for the discrimination between miRNAs and other sequences. Positive training data for model establishment, for the most part, stems from miRBase, the miRNA registry. The quality of the entries in miRBase has been questioned, though. This unknown quality led to the development of filtering strategies in attempts to produce high quality positive datasets which can lead to a scarcity of positive data. To analyze the quality of filtered data we developed a machine learning model and found it is well able to establish data quality based on intrinsic measures. Additionally, we analyzed which features describing pre-miRNAs could discriminate between low and high quality data. Both models are applicable to data from miRBase and can be used for establishing high quality positive data. This will facilitate the development of better miRNA detection tools which will make the prediction of miRNAs in disease states more accurate. Finally, we applied both models to all miRBase data and provide the list of high quality hairpins.
ISSN: 1613-4516
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 SizeFormat 
Improving-the-Quality.pdf1.65 MBAdobe PDFView/Open
Show full item record

CORE Recommender


checked on Nov 25, 2023


checked on Jun 17, 2023

Page view(s)

checked on Nov 27, 2023


checked on Nov 27, 2023

Google ScholarTM



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