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Random Forest in Splice Site Prediction of Human Genome
Book chapter

Random Forest in Splice Site Prediction of Human Genome

Elham Pashaei, Mustafa Ozen, Nizamettin Aydin and Dov Jaron
XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016, pp 518-523
17 Sep 2016
DOI: https://doi.org/10.1007/978-3-319-32703-7_100
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Feature ranking Random forest Splice site prediction
With the rapid growth of huge amounts of DNA sequence, genes identification has become an important task in bioinformatics. To detect genes, it is important to accurately predict splice sites, i.e. exon intron boundaries. Moreover, in biology where structures are described by a large number of features as splice sites, the feature selection is an important step toward the classification task. It provides useful biological knowledge and allows for a faster and better classification. Feature selection techniques can be divided into two groups: feature-ranking and feature-subset selection. This paper investigates the performance of combining support vector machine (SVM) with two different feature ranking methods, namely F-score and Random Forest feature ranking competitively in splice site detection of Human genome. Also a new classification method based on Random Forest for splice site prediction is presented.

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UN Sustainable Development Goals (SDGs)

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#3 Good Health and Well-Being

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Web of Science research areas
Engineering, Biomedical
Materials Science, Biomaterials
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