Feature selection for the prediction of translation initiation sites
Translation initiation sites (TISs) are important signals in cDNA sequences. In many previous attempts to predict TISs in cDNA sequences, three major factors affect the prediction performance: the nature of the cDNA sequence sets, the relevant features selected, and the classification methods used....
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sg-smu-ink.sis_research-40172016-02-05T06:30:05Z Feature selection for the prediction of translation initiation sites Li G., Tze-Yun LEONG, Translation initiation sites (TISs) are important signals in cDNA sequences. In many previous attempts to predict TISs in cDNA sequences, three major factors affect the prediction performance: the nature of the cDNA sequence sets, the relevant features selected, and the classification methods used. In this paper, we examine different approaches to select and integrate relevant features for TIS prediction. The top selected significant features include the features from the position weight matrix and the propensity matrix, the number of nucleotide C in the sequence downstream ATG, the number of downstream stop codons, the number of upstream ATGs, and the number of some amino acids, such as amino acids A and D. With the numerical data generated from these features, different classification methods, including decision tree, naïve Bayes, and support vector machine, were applied to three independent sequence sets. The identified significant features were found to be biologically meaningful, while the experiments showed promising results. 2005-05-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/3017 info:doi/10.1504/IJVD.2005.007220 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Classification Feature selection Translation initiation site prediction Computer Sciences Health Information Technology |
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Translation initiation sites (TISs) are important signals in cDNA sequences. In many previous attempts to predict TISs in cDNA sequences, three major factors affect the prediction performance: the nature of the cDNA sequence sets, the relevant features selected, and the classification methods used. In this paper, we examine different approaches to select and integrate relevant features for TIS prediction. The top selected significant features include the features from the position weight matrix and the propensity matrix, the number of nucleotide C in the sequence downstream ATG, the number of downstream stop codons, the number of upstream ATGs, and the number of some amino acids, such as amino acids A and D. With the numerical data generated from these features, different classification methods, including decision tree, naïve Bayes, and support vector machine, were applied to three independent sequence sets. The identified significant features were found to be biologically meaningful, while the experiments showed promising results. |
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Li G., Tze-Yun LEONG, |
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Li G., Tze-Yun LEONG, |
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Li G., |
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Feature selection for the prediction of translation initiation sites |
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Feature selection for the prediction of translation initiation sites |
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Feature selection for the prediction of translation initiation sites |
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Feature selection for the prediction of translation initiation sites |
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Feature selection for the prediction of translation initiation sites |
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feature selection for the prediction of translation initiation sites |
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2005 |
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