Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5791
Title: Feature selection has a large impact on one-class classification accuracy for micrornas in plants
Authors: Yousef, Malik
Demirci, Müşerref Duygu Saçar
Khalifa, Waleed
Allmer, Jens
Demirci, Müşerref Duygu Saçar
Allmer, Jens
Izmir Institute of Technology. Molecular Biology and Genetics
Keywords: MicroRNAs
miRNA detection
Machine learning
Classification
Plant
Issue Date: 2016
Publisher: Hindawi Publishing Corporation
Source: Yousef, M., Saçar Demirci, M. D., Khalifa, W., and Allmer, J. (2016). Feature selection has a large impact on one-class classification accuracy for micrornas in plants. Advances in Bioinformatics, 2016. doi:10.1155/2016/5670851
Abstract: MicroRNAs (miRNAs) are short RNA sequences involved in posttranscriptional gene regulation. Their experimental analysis is complicated and, therefore, needs to be supplemented with computational miRNA detection. Currently computational miRNA detection is mainly performed using machine learning and in particular two-class classification. For machine learning, the miRNAs need to be parametrized and more than 700 features have been described. Positive training examples for machine learning are readily available, but negative data is hard to come by. Therefore, it seems prerogative to use one-class classification instead of two-class classification. Previously, we were able to almost reach two-class classification accuracy using one-class classifiers. In this work, we employ feature selection procedures in conjunction with one-class classification and show that there is up to 36% difference in accuracy among these feature selection methods. The best feature set allowed the training of a one-class classifier which achieved an average accuracy of 95.6% thereby outperforming previous two-class-based plant miRNA detection approaches by about 0.5%. We believe that this can be improved upon in the future by rigorous filtering of the positive training examples and by improving current feature clustering algorithms to better target pre-miRNA feature selection.
URI: http://doi.org/10.1155/2016/5670851
http://hdl.handle.net/11147/5791
ISSN: 1687-8027
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

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