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    An in-silico method for prediction of polyadenylation signals in human sequences.

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    This paper presents a machine learning method to predict polyadenylation signals (PASes) in human DNA and mRNA sequences by analysing features around them. This method consists of three sequential steps of feature manipulation: generation, selection and integration of features. In the first step, new features are generated using k-gram nucleotide acid or amino acid patterns. In the second step, a number of important features are selected by an entropy-based algorithm. In the third step, support vector machines are employed to recognize true PASes from a large number of candidates. Our study shows that true PASes in DNA and mRNA sequences can be characterized by different features, and also shows that both upstream and downstream sequence elements are important for recognizing PASes from DNA sequences. We tested our method on several public data sets as well as our own extracted data sets. In most cases, we achieved better validation results than those reported previously on the same data sets. The important motifs observed are highly consistent with those reported in literature.

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    Description

    Title : An in-silico method for prediction of polyadenylation signals in human sequences.
    Author(s) : Huiqing Liu, Hao Han, Jinyan Li, Limsoon Wong
    Abstract : This paper presents a machine learning method to predict polyadenylation signals (PASes) in human DNA and mRNA sequences by analysing features around them. This method consists of three sequential steps of feature manipulation: generation, selection and integration of features. In the first step, new features are generated using k-gram nucleotide acid or amino acid patterns. In the second step, a number of important features are selected by an entropy-based algorithm. In the third step, support vector machines are employed to recognize true PASes from a large number of candidates. Our study shows that true PASes in DNA and mRNA sequences can be characterized by different features, and also shows that both upstream and downstream sequence elements are important for recognizing PASes from DNA sequences. We tested our method on several public data sets as well as our own extracted data sets. In most cases, we achieved better validation results than those reported previously on the same data sets. The important motifs observed are highly consistent with those reported in literature.
    Subject : unspecified
    Area : Other
    Language : English
    Year : 2003

    Affiliations Dept of Computer Science, National University of Singapore
    Journal : Genome informatics International Conference on Genome Informatic
    Volume : 14
    Issue : 14
    Pages : 84-93
    Url : http://api.mendeley.com/research/insilico-method-prediction-polyadenylation-signals-human-sequences/

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    Limsoon's Peer Evaluation activity

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