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    Using feature generation and feature selection for accurate prediction of translation initiation sites.

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    Correct prediction of the translation initiation site (TIS) is an important issue in genomic research. We show that feature generation together with correlation based feature selection can be used with a variety of machine learning algorithms to give highly accurate translation initiation site prediction. Only very few features are needed and the results achieve comparable accuracy to the best existing approaches. Our approach has the advantage that it does not require one to devise a special prediction method; rather standard machine learning classifiers are shown to give very good performance on the selected features. The raw and generated features which we have found to be important are the following: positions -3 and -1 in the sequence; upstream k-grams for k=3, 4, and 5; stop-codon frequency; downstream in-frame 3-gram; and the distance of ATG to the beginning of the sequence. The best result, with an overall accuracy of 90%, is obtained by selecting only seven features from this set. The same features retrained with the use of a scanning model achieves an overall accuracy of 94% on this dataset.

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    Description

    Title : Using feature generation and feature selection for accurate prediction of translation initiation sites.
    Author(s) : Fanfan Zeng, Roland H C Yap, Limsoon Wong
    Abstract : Correct prediction of the translation initiation site (TIS) is an important issue in genomic research. We show that feature generation together with correlation based feature selection can be used with a variety of machine learning algorithms to give highly accurate translation initiation site prediction. Only very few features are needed and the results achieve comparable accuracy to the best existing approaches. Our approach has the advantage that it does not require one to devise a special prediction method; rather standard machine learning classifiers are shown to give very good performance on the selected features. The raw and generated features which we have found to be important are the following: positions -3 and -1 in the sequence; upstream k-grams for k=3, 4, and 5; stop-codon frequency; downstream in-frame 3-gram; and the distance of ATG to the beginning of the sequence. The best result, with an overall accuracy of 90%, is obtained by selecting only seven features from this set. The same features retrained with the use of a scanning model achieves an overall accuracy of 94% on this dataset.
    Keywords : codon, initiator, computational biology, computational biology methods, sequence analysis, dna

    Subject : unspecified
    Area : Other
    Language : English
    Year : 2002

    Affiliations Dept of Computer Science, National University of Singapore
    Journal : Genome informatics International Conference on Genome Informatic
    Volume : 13
    Pages : 192-200
    Url : http://www.ncbi.nlm.nih.gov/pubmed/14571388

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    Limsoon's Peer Evaluation activity

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