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Markovian encoding models in human splice site recognition using SVM.

Abstract：

:Splice site recognition is among the most significant and challenging tasks in bioinformatics due to its key role in gene annotation. Effective prediction of splice site requires nucleotide encoding methods that reveal the characteristics of DNA sequences to provide appropriate features to serve as input of machine learning classifiers. Markovian models are the most influential encoding methods that highly used for pattern recognition in biological data. However, a direct performance comparison of these methods in splice site domain has not been assessed yet. This study compares various Markovian encoding models for splice site prediction utilizing support vector machine, as the most outstanding learning method in the domain, and conducts a new precise evaluation of Markovian approaches that corrects this limitation. Moreover, a novel sequence encoding approach based on third order Markov model (MM3) is proposed. The experimental results show that the proposed method, namely MM3-SVM, performs significantly better than thirteen best known state-of-the-art algorithms, while tested on HS3D dataset considering several performance criteria. Further, it achieved higher prediction accuracy than several well-known tools like NNsplice, MEM, MM1, WMM, and GeneID, using an independent test set of 50 genes. We also developed MMSVM, a web tool to predict splice sites in any human sequence using the proposed approach. The MMSVM web server can be assessed at https://pashaei.shinyapps.io/mmsvm.

journal_name

Comput Biol Chem

journal_title

Computational biology and chemistry

authors

Pashaei E,Aydin N

doi

10.1016/j.compbiolchem.2018.02.005

subject

Has Abstract

pub_date

2018-04-01 00:00:00

pages

159-170

eissn

1476-9271

issn

1476-928X

pii

S1476-9271(17)30363-8

journal_volume

73

pub_type

杂志文章

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