Title

Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences

Publication Type

Journal Article

Publication Date

8-2005

Abstract

Translation initiation sites (TISs) are important signals in cDNA sequences. Many research efforts have tried to predict TISs in cDNA sequences. In this paper, we propose to use mixture Gaussian models for TIS prediction. Using both local features and some features generated from global measures, the proposed method predicts TISs with a sensitivity of 98 percent and a specificity of 93.6 percent. Our method outperforms many other existing methods in sensitivity while keeping specificity high. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models. © 2005 IEEE.

Keywords

Bioinformatics, Classification, Feature extraction, Mixture Gaussian model, Translation initiation sites

Discipline

Numerical Analysis and Scientific Computing

Publication

IEEE Transactions on Knowledge and Data Engineering

Volume

17

Issue

8

First Page

1152

Last Page

1160

ISSN

1041-4347

Identifier

10.1109/TKDE.2005.133

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

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