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
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 appr
oach 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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