Feature Selection for MicroRNA Target Prediction - Comparison of One-Class Feature Selection Methodologies

Malik Yousef, Jens Allmer, Waleed Khalifa

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.

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Paper Citation


in Harvard Style

Yousef M., Allmer J. and Khalifa W. (2016). Feature Selection for MicroRNA Target Prediction - Comparison of One-Class Feature Selection Methodologies . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 216-225. DOI: 10.5220/0005701602160225


in Bibtex Style

@conference{bioinformatics16,
author={Malik Yousef and Jens Allmer and Waleed Khalifa},
title={Feature Selection for MicroRNA Target Prediction - Comparison of One-Class Feature Selection Methodologies},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016)},
year={2016},
pages={216-225},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005701602160225},
isbn={978-989-758-170-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016)
TI - Feature Selection for MicroRNA Target Prediction - Comparison of One-Class Feature Selection Methodologies
SN - 978-989-758-170-0
AU - Yousef M.
AU - Allmer J.
AU - Khalifa W.
PY - 2016
SP - 216
EP - 225
DO - 10.5220/0005701602160225