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  • Importance of Dimensionality Reduction in Protein Fold Recognition

    Author(s)
    Sharma, Alok
    Sharma, Ronesh
    Dehzangi, Abdollah
    Lyons, James
    Paliwal, Kuldip
    Tsunoda, Tatsuhiko
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Year published
    2015
    Metadata
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    Abstract
    Interpreting tertiary structure of a protein has been a crucial task in the field of biosciences. This problem can be addressed by detecting protein folds which is considered as an intermediate step in the tertiary structure prediction. From the perspective of computational sciences, the protein fold recognition can be subdivided in two steps: 1) feature extraction of protein sequences, and 2) identifying extracted features using appropriate classifiers. These steps are important to accurately identify folds of a novel protein sequence. In order to fully characterize a protein sequence, the number of features required is ...
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    Interpreting tertiary structure of a protein has been a crucial task in the field of biosciences. This problem can be addressed by detecting protein folds which is considered as an intermediate step in the tertiary structure prediction. From the perspective of computational sciences, the protein fold recognition can be subdivided in two steps: 1) feature extraction of protein sequences, and 2) identifying extracted features using appropriate classifiers. These steps are important to accurately identify folds of a novel protein sequence. In order to fully characterize a protein sequence, the number of features required is large and sometimes even unmanageable. This high dimensionality of features is difficult to process using conventional classifiers. Therefore, it is a challenge to develop and apply dimensionality reduction techniques for protein fold recognition. In this paper, we have emphasized the importance of dimensionality reduction techniques (DRTs) for protein fold recognition. To narrate, we have compared the recognition performance without DRT and with DRT on 3 benchmark datasets.
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    Conference Title
    2015 2ND ASIA-PACIFIC WORLD CONGRESS ON COMPUTER SCIENCE AND ENGINEERING (APWC ON CSE 2015)
    DOI
    https://doi.org/10.1109/APWCCSE.2015.7476132
    Subject
    Chemical engineering not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/123655
    Collection
    • Conference outputs

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