Translation initiation sites prediction with mixture gaussian models
Translation initiation sites (TIS) are important signals in cDNA sequences. Many research efforts have tried to predict TIS in cDNA sequences. In this paper, we propose using mixture Gaussian models to predict TIS in cDNA sequences. Some new global measures are used to generate numerical features fr...
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sg-smu-ink.sis_research-40502016-02-05T06:30:05Z Translation initiation sites prediction with mixture gaussian models Li, Guoliang Tze-Yun LEONG, Zhang, Louxin Translation initiation sites (TIS) are important signals in cDNA sequences. Many research efforts have tried to predict TIS in cDNA sequences. In this paper, we propose using mixture Gaussian models to predict TIS in cDNA sequences. Some new global measures are used to generate numerical features from cDNA sequences, such as the length of the open reading frame downstream from ATG, the number of other ATGs upstream and downstream from the current ATGs, etc. With these global features, the proposed method predicts TIS with sensitivity 98% and specificity 92%. The sensitivity is much better than that from other methods. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models. © Springer-Verlag 2004. 2004-12-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/3050 info:doi/10.1007/978-3-540-30219-3_29 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial intelligence Bioinformatics Forecasting Mixtures Numerical methods Translation (languages) cDNA sequence Global feature Global measures Mixture Gaussian model Numerical features Open reading frame Research efforts Translation initiation site Numerical Analysis and Scientific Computing |
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Artificial intelligence Bioinformatics Forecasting Mixtures Numerical methods Translation (languages) cDNA sequence Global feature Global measures Mixture Gaussian model Numerical features Open reading frame Research efforts Translation initiation site Numerical Analysis and Scientific Computing |
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Artificial intelligence Bioinformatics Forecasting Mixtures Numerical methods Translation (languages) cDNA sequence Global feature Global measures Mixture Gaussian model Numerical features Open reading frame Research efforts Translation initiation site Numerical Analysis and Scientific Computing Li, Guoliang Tze-Yun LEONG, Zhang, Louxin Translation initiation sites prediction with mixture gaussian models |
| description |
Translation initiation sites (TIS) are important signals in cDNA sequences. Many research efforts have tried to predict TIS in cDNA sequences. In this paper, we propose using mixture Gaussian models to predict TIS in cDNA sequences. Some new global measures are used to generate numerical features from cDNA sequences, such as the length of the open reading frame downstream from ATG, the number of other ATGs upstream and downstream from the current ATGs, etc. With these global features, the proposed method predicts TIS with sensitivity 98% and specificity 92%. The sensitivity is much better than that from other methods. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models. © Springer-Verlag 2004. |
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text |
| author |
Li, Guoliang Tze-Yun LEONG, Zhang, Louxin |
| author_facet |
Li, Guoliang Tze-Yun LEONG, Zhang, Louxin |
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Li, Guoliang |
| title |
Translation initiation sites prediction with mixture gaussian models |
| title_short |
Translation initiation sites prediction with mixture gaussian models |
| title_full |
Translation initiation sites prediction with mixture gaussian models |
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Translation initiation sites prediction with mixture gaussian models |
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Translation initiation sites prediction with mixture gaussian models |
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translation initiation sites prediction with mixture gaussian models |
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Institutional Knowledge at Singapore Management University |
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2004 |
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https://ink.library.smu.edu.sg/sis_research/3050 |
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