Title

Feature selection for the prediction of translation initiation sites

Publication Type

Journal Article

Publication Date

5-2005

Abstract

Translation initiation sites (TISs) are important signals in cDNA sequences. In many previous attempts to predict TISs in cDNA sequences, three major factors affect the prediction performance: the nature of the cDNA sequence sets, the relevant features selected, and the classification methods used. In this paper, we examine different approaches to select and integrate relevant features for TIS prediction. The top selected significant features include the features from the position weight matrix and the propensity matrix, the number of nucleotide C in the sequence downstream ATG, the number of downstream stop codons, the number of upstream ATGs, and the number of some amino acids, such as amino acids A and D. With the numerical data generated from these features, different classification methods, including decision tree, naïve Bayes, and support vector machine, were applied to three independent sequence sets. The identified significant features were found to be biologically meaningful, while the experiments showed promising results.

Keywords

Classification, Feature selection, Translation initiation site prediction

Discipline

Computer Sciences | Health Information Technology

Research Areas

Intelligent Systems and Decision Analytics

Publication

Genomics, Proteomics and Bioinformatics

Volume

3

Issue

2

First Page

73

Last Page

83

ISSN

1672-0229

Identifier

10.1504/IJVD.2005.007220

Publisher

Elsevier

This document is currently not available here.

Share

COinS