Random forest in Splice site prediction of human genome

Elham Pashaei*, Mustafa Ozen, Nizamettin Aydin

*Bu çalışma için yazışmadan sorumlu yazar

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4 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıXIV Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016
EditörlerEfthyvoulos Kyriacou, Stelios Christofides, Constantinos S. Pattichis
YayınlayanSpringer Verlag
Sayfalar512-517
Sayfa sayısı6
ISBN (Basılı)9783319327013
DOI'lar
Yayın durumuYayınlandı - 2016
Harici olarak yayınlandıEvet
Etkinlik14th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016 - Paphos, Cyprus
Süre: 31 Mar 20162 Nis 2016

Yayın serisi

AdıIFMBE Proceedings
Hacim57
ISSN (Basılı)1680-0737

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???event.eventtypes.event.conference???14th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016
Ülke/BölgeCyprus
ŞehirPaphos
Periyot31/03/162/04/16

Bibliyografik not

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

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