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Research Papers:

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

Balachandran Manavalan _, Tae Hwan Shin and Gwang Lee

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Oncotarget. 2018; 9:1944-1956. https://doi.org/10.18632/oncotarget.23099

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Abstract

Balachandran Manavalan1, Tae Hwan Shin1,2 and Gwang Lee1,2

1Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea

2Institute of Molecular Science and Technology, Ajou University, Suwon, Republic of Korea

Correspondence to:

Balachandran Manavalan, email: bala@ajou.ac.kr

Gwang Lee, email: glee@ajou.ac.kr

Keywords: DNase I hypersensitive site; feature selection; machine learning; random forest; support vector machine

Received: September 06, 2017     Accepted: November 17, 2017     Published: December 08, 2017

ABSTRACT

DNase I hypersensitive sites (DHSs) are genomic regions that provide important information regarding the presence of transcriptional regulatory elements and the state of chromatin. Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms. Although many experimental methods have been proposed to identify DHSs, they have proven to be expensive for genome-wide application. Therefore, it is necessary to develop computational methods for DHS prediction. In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal features. The optimal combination of features was identified from a large set that included nucleotide composition and di- and trinucleotide physicochemical properties, using a random forest algorithm. DHSpred achieved a Matthews correlation coefficient and accuracy of 0.660 and 0.871, respectively, which were 3% higher than those of control SVM predictors trained with non-optimized features, indicating the efficiency of the feature selection method. Furthermore, the performance of DHSpred was superior to that of state-of-the-art predictors. An online prediction server has been developed to assist the scientific community, and is freely available at: http://www.thegleelab.org/DHSpred.html.


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