Random forest in Splice site prediction of human genome

Elham Pashaei; Mustafa Ozen; Nizamettin Aydin

doi:10.1007/978-3-319-32703-7_99

Random forest in Splice site prediction of human genome

Elham Pashaei^*, Mustafa Ozen, Nizamettin Aydin

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

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. exonintron 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 Fscore 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.

Original language	English
Title of host publication	XIV Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016
Editors	Efthyvoulos Kyriacou, Stelios Christofides, Constantinos S. Pattichis
Publisher	Springer Verlag
Pages	512-517
Number of pages	6
ISBN (Print)	9783319327013
DOIs	https://doi.org/10.1007/978-3-319-32703-7_99
Publication status	Published - 2016
Externally published	Yes
Event	14th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016 - Paphos, Cyprus Duration: 31 Mar 2016 → 2 Apr 2016

Publication series

Name	IFMBE Proceedings
Volume	57
ISSN (Print)	1680-0737

Conference

Conference	14th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016
Country/Territory	Cyprus
City	Paphos
Period	31/03/16 → 2/04/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

Keywords

Feature ranking
Random Forest
Splice site prediction

Access to Document

10.1007/978-3-319-32703-7_99

Cite this

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title = "Random forest in Splice site prediction of human genome",

abstract = "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. exonintron 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 Fscore 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.",

keywords = "Feature ranking, Random Forest, Splice site prediction",

author = "Elham Pashaei and Mustafa Ozen and Nizamettin Aydin",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 14th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016 ; Conference date: 31-03-2016 Through 02-04-2016",

year = "2016",

doi = "10.1007/978-3-319-32703-7_99",

language = "English",

isbn = "9783319327013",

series = "IFMBE Proceedings",

publisher = "Springer Verlag",

pages = "512--517",

editor = "Efthyvoulos Kyriacou and Stelios Christofides and Pattichis, {Constantinos S.}",

booktitle = "XIV Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016",

address = "Germany",

}

Pashaei, E, Ozen, M & Aydin, N 2016, Random forest in Splice site prediction of human genome. in E Kyriacou, S Christofides & CS Pattichis (eds), XIV Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016. IFMBE Proceedings, vol. 57, Springer Verlag, pp. 512-517, 14th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016, Paphos, Cyprus, 31/03/16. https://doi.org/10.1007/978-3-319-32703-7_99

Random forest in Splice site prediction of human genome. / Pashaei, Elham; Ozen, Mustafa; Aydin, Nizamettin.
XIV Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016. ed. / Efthyvoulos Kyriacou; Stelios Christofides; Constantinos S. Pattichis. Springer Verlag, 2016. p. 512-517 (IFMBE Proceedings; Vol. 57).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Random forest in Splice site prediction of human genome

AU - Pashaei, Elham

AU - Ozen, Mustafa

AU - Aydin, Nizamettin

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - 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. exonintron 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 Fscore 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.

AB - 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. exonintron 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 Fscore 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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SN - 9783319327013

T3 - IFMBE Proceedings

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EP - 517

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A2 - Kyriacou, Efthyvoulos

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ER -

Random forest in Splice site prediction of human genome

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Keywords

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