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

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

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, th...

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Bibliographic Details
Main Authors: Li, G., Tze-Yun LEONG, Zhang, Louxin
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2005
Subjects:
Bioinformatics
Classification
Feature extraction
Mixture Gaussian model
Translation initiation sites
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/3051
https://ink.library.smu.edu.sg/context/sis_research/article/4051/viewcontent/Translation_initiation_sites_prediction_with_mixture_Gaussian_models_in_human_cDNA_sequences.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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