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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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

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spelling	sg-smu-ink.sis_research-40512019-04-02T02:23:58Z Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences Li, G. Tze-Yun LEONG, Zhang, Louxin 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. 2005-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3051 info:doi/10.1109/TKDE.2005.133 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 http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Bioinformatics Classification Feature extraction Mixture Gaussian model Translation initiation sites Numerical Analysis and Scientific Computing
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Bioinformatics Classification Feature extraction Mixture Gaussian model Translation initiation sites Numerical Analysis and Scientific Computing
spellingShingle	Bioinformatics Classification Feature extraction Mixture Gaussian model Translation initiation sites Numerical Analysis and Scientific Computing Li, G. Tze-Yun LEONG, Zhang, Louxin Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences
description	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.
format	text
author	Li, G. Tze-Yun LEONG, Zhang, Louxin
author_facet	Li, G. Tze-Yun LEONG, Zhang, Louxin
author_sort	Li, G.
title	Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences
title_short	Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences
title_full	Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences
title_fullStr	Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences
title_full_unstemmed	Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences
title_sort	translation initiation sites prediction with mixture gaussian models in human cdna sequences
publisher	Institutional Knowledge at Singapore Management University
publishDate	2005
url	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
_version_	1770572791598284800

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

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