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  • Protein fold recognition using segmentation-based feature extraction model

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    Author(s)
    Dehzangi, Abdollah
    Sattar, Abdul
    Griffith University Author(s)
    Sattar, Abdul
    Year published
    2013
    Metadata
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    Abstract
    Protein Fold recognition (PFR) is considered as an important step towards protein structure prediction. It also provides significant information about general functionality of a given protein. Despite all the efforts have been made, PFR still remains unsolved. It is shown that appropriately extracted features from the physicochemical-based attributes of the amino acids plays crucial role to address this problem. In this study, we explore 55 different physicochemical-based attributes using two novel feature extraction methods namely segmented distribution and segmented density. Then, by proposing an ensemble of different ...
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    Protein Fold recognition (PFR) is considered as an important step towards protein structure prediction. It also provides significant information about general functionality of a given protein. Despite all the efforts have been made, PFR still remains unsolved. It is shown that appropriately extracted features from the physicochemical-based attributes of the amino acids plays crucial role to address this problem. In this study, we explore 55 different physicochemical-based attributes using two novel feature extraction methods namely segmented distribution and segmented density. Then, by proposing an ensemble of different classifiers based on the AdaBoost.M1 and Support Vector Machine (SVM) classifiers which are diversely trained on different combinations of features extracted from these attributes, we outperform similar studies found in the literature for over 2% for the PFR task.
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    Conference Title
    INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2013), PT I,
    Volume
    7802
    Issue
    PART 1
    Publisher URI
    http://seminar.utmspace.edu.my/aciids2013/
    DOI
    https://doi.org/10.1007/978-3-642-36546-1_36
    Copyright Statement
    © 2013 Springer-Verlag Berlin Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
    Subject
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/58746
    Collection
    • Conference outputs

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