Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/6284
Title: Delineating the impact of machine learning elements in pre-microRNA detection
Authors: Saçar Demirci, Müşerref Duygu
Allmer, Jens
Saçar Demirci, Müşerref Duygu
Allmer, Jens
Izmir Institute of Technology. Molecular Biology and Genetics
Keywords: Feature selection
MicroRNAs
ML strategy
Negative dataset
Issue Date: 2017
Publisher: PeerJ Inc.
Source: Saçar Demirci, M. D., and Allmer, J. (2017). Delineating the impact of machine learning elements in pre-microRNA detection. PeerJ, 2017(3). doi:10.7717/peerj.3131
Abstract: Gene regulation modulates RNA expression via transcription factors. Posttranscriptional gene regulation in turn influences the amount of protein product through, for example, microRNAs (miRNAs). Experimental establishment of miRNAs and their effects is complicated and even futile when aiming to establish the entirety of miRNA target interactions. Therefore, computational approaches have been proposed. Many such tools rely on machine learning (ML) which involves example selection, feature extraction, model training, algorithm selection, and parameter optimization. Different ML algorithms have been used for model training on various example sets, more than 1,000 features describing pre-miRNAs have been proposed and different training and testing schemes have been used for model establishment. For pre-miRNA detection, negative examples cannot easily be established causing a problem for two class classification algorithms. There is also no consensus on what ML approach works best and, therefore, we set forth and established the impact of the different parts involved in ML on model performance. Furthermore, we established two new negative datasets and analyzed the impact of using them for training and testing. It was our aim to attach an order of importance to the parts involved in ML for pre-miRNA detection, but instead we found that all parts are intricately connected and their contributions cannot be easily untangled leading us to suggest that when attempting ML-based pre-miRNA detection many scenarios need to be explored.
URI: http://doi.org/10.7717/peerj.3131
http://hdl.handle.net/11147/6284
ISSN: 2167-8359
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

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