Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5792
Title: Feature selection for microRNA target prediction comparison of one-class feature selection methodologies
Authors: Yousef, Malik
Allmer, Jens
Khalifa, Waleed
Allmer, Jens
Izmir Institute of Technology. Molecular Biology and Genetics
Keywords: Feature selection
Machine learning
MicroRNA targets
Classification
Bioinformatics
Issue Date: 2016
Publisher: Hindawi Publishing Corporation
Source: Yousef, M., Allmer, J., and Khalifa, W. (2016, February 21-23). Feature selection for microRNA target prediction comparison of one-class feature selection methodologies. Paper presented at the 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016. doi:10.5220/0005701602160225
Abstract: Traditionally, machine learning algorithms build classification models from positive and negative examples. Recently, one-class classification (OCC) receives increasing attention in machine learning for problems where the negative class cannot be defined unambiguously. This is specifically problematic in bioinformatics since for some important biological problems the target class (positive class) is easy to obtain while the negative one cannot be measured. Artificially generating the negative class data can be based on unreliable assumptions. Several studies have applied two-class machine learning to predict microRNAs (miRNAs) and their target. Different approaches for the generation of an artificial negative class have been applied, but may lead to a biased performance estimate. Feature selection has been well studied for the two-class classification problem, while fewer methods are available for feature selection in respect to OCC. In this study, we present a feature selection approach for applying one-class classification to the prediction of miRNA targets. A comparison between one-class and two-class approaches is presented to highlight that their performance are similar while one-class classification is not based on questionable artificial data for training and performance evaluation. We further show that the feature selection method we tried works to a degree, but needs improvement in the future. Perhaps it could be combined with other approaches.
Description: 7th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2016 - Part of 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016; Rome; Italy; 21 February 2016 through 23 February 2016
URI: http://doi.org/10.5220/0005701602160225
http://hdl.handle.net/11147/5792
ISBN: 9789897581700
Appears in Collections:Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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