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  • Predicting the Secondary Structure of Proteins by Cascading Neural Networks

    Author(s)
    Alirezaee, Maryam
    Dehzangi, Iman
    Mansoori, Eghbal
    Griffith University Author(s)
    Dehzangi, Iman
    Year published
    2012
    Metadata
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    Abstract
    Protein Secondary Structure Prediction (PSSP) is considered as a challenging task in bioinformatics and so many approaches have been proposed in the literature to solve this problem via achieving more accurate prediction results. Accurate prediction of secondary structure is a critical role in deducing tertiary structure of proteins and their functions. Among the proposed approaches to tackle this problem, Artificial Neural Networks (ANNs) are considered as one of the successful tools that are widely used in this field. Recently, many efforts have been devoted to modify, improve and combine this methodology with other machine ...
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    Protein Secondary Structure Prediction (PSSP) is considered as a challenging task in bioinformatics and so many approaches have been proposed in the literature to solve this problem via achieving more accurate prediction results. Accurate prediction of secondary structure is a critical role in deducing tertiary structure of proteins and their functions. Among the proposed approaches to tackle this problem, Artificial Neural Networks (ANNs) are considered as one of the successful tools that are widely used in this field. Recently, many efforts have been devoted to modify, improve and combine this methodology with other machine learning methods in order to get better results. In this work, we have proposed a two-stage feed forward neural network for prediction of protein secondary structures. To evaluate our approach, it is applied on RS126 dataset and its results are compared with some other NN-based methods.
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    Journal Title
    International Journal of Artificial Intelligence & Applications
    Volume
    3
    Issue
    6
    DOI
    https://doi.org/10.5121/ijaia.2012.3605
    Subject
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/49587
    Collection
    • Journal articles

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