Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5322
Title: Data mining for microrna gene prediction: On the impact of class imbalance and feature number for microrna gene prediction
Authors: Saçar, Müşerref Duygu
Allmer, Jens
Saçar, Müşerref Duygu
Allmer, Jens
Izmir Institute of Technology. Molecular Biology and Genetics
Keywords: Class imbalance
Data mining
Feature selection
Machine learning
MicroRNAs
MiRNA gene prediction
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Saçar, M. D., and Allmer, J. (2013, September 25-27). Data mining for microrna gene prediction: On the impact of class imbalance and feature number for microrna gene prediction. Paper presented at the 8th International Symposium on Health Informatics and Bioinformatics. doi:10.1109/HIBIT.2013.6661685
Abstract: MicroRNAs (miRNAs) are small, non-coding RNAs which are involved in the posttranscriptional modulation of gene expression. Their short (18-24) single stranded mature sequences are involved in targeting specific genes. It turns out that experimental methods are limited and that it is difficult, if not impossible, to establish all miRNAs and their targets experimentally. Therefore, many tools for the prediction of miRNA genes and miRNA targets have been proposed. Most of these tools are based on machine learning methods and within that area mostly two-class classification is employed. Unfortunately, truly negative data is impossible to attain and only approximations of negative data are currently available. Also, we recently showed that the available positive data is not flawless. Here we investigate the impact of class imbalance on the learner accuracy and find that there is a difference of up to 50% between the best and worst precision and recall values. In addition, we looked at increasing number of features and found a curve maximizing at 0.97 recall and 0.91 precision with quickly decaying performance after inclusion of more than 100 features. © 2013 IEEE.
Description: 8th International Symposium on Health Informatics and Bioinformatics, HIBIT 2013; Ankara; Turkey; 25 September 2013 through 27 September 2013
URI: http://doi.org/10.1109/HIBIT.2013.6661685
http://hdl.handle.net/11147/5322
ISBN: 9781479907014
Appears in Collections:Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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