loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Malik Yousef 1 ; Jens Allmer 2 and Waleed Khalifa 1

Affiliations: 1 The College of Sakhnin and The Galilee Society, Israel ; 2 Izmir Institute of Technology and IZTEKGEB, Turkey

ISBN: 978-989-758-170-0

Keyword(s): MicroRNA Targets, One-Class, Two-Classes, Machine Learning, Feature Selection.

Related Ontology Subjects/Areas/Topics: Bioinformatics ; Biomedical Engineering ; Data Mining and Machine Learning ; Pattern Recognition, Clustering and Classification ; Sequence Analysis ; Transcriptomics

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 appro ach 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. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.84.139.101

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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

@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},
}

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.