Publication Type
Journal Article
Version
publishedVersion
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)
Citation
Li, G.; Tze-Yun LEONG; and Zhang, Louxin.
Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences. (2005). IEEE Transactions on Knowledge and Data Engineering. 17, (8), 1152-1160.
Available at: https://ink.library.smu.edu.sg/sis_research/3051
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://api.elsevier.com/content/abstract/scopus_id/24344510329