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  • DFS Based Partial Pathways in GA for Protein Structure Prediction

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    53002_1.pdf (577.1Kb)
    Author(s)
    Hoque, Md Tamjidul
    Chetty, Madhu
    Lewis, Andrew
    Sattar, Abdul
    Griffith University Author(s)
    Sattar, Abdul
    Lewis, Andrew J.
    Year published
    2008
    Metadata
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    Abstract
    Nondeterministic conformational search techniques, such as Genetic Algorithms (GAs) are promising for solving protein structure prediction (PSP) problem. The crossover operator of a GA can underpin the formation of potential conformations by exchanging and sharing potential sub-conformations, which is promising for solving PSP. However, the usual nature of an optimum PSP conformation being compact can produce many invalid conformations (by having non-self-avoiding-walk) using crossover. While a crossover-based converging conformation suffers from limited pathways, combining it with depth-first search (DFS) can partially ...
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    Nondeterministic conformational search techniques, such as Genetic Algorithms (GAs) are promising for solving protein structure prediction (PSP) problem. The crossover operator of a GA can underpin the formation of potential conformations by exchanging and sharing potential sub-conformations, which is promising for solving PSP. However, the usual nature of an optimum PSP conformation being compact can produce many invalid conformations (by having non-self-avoiding-walk) using crossover. While a crossover-based converging conformation suffers from limited pathways, combining it with depth-first search (DFS) can partially reveal potential pathways. DFS generates random conformations increasingly quickly with increasing length of the protein sequences compared to random-move-only-based conformation generation. Random conformations are frequently applied for maintaining diversity as well as for initialization in many GA variations.
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    Conference Title
    PATTERN RECOGNITION IN BIOINFORMATICS, PROCEEDINGS
    Volume
    5265
    Publisher URI
    https://www.springer.com/gp
    DOI
    https://doi.org/10.1007/978-3-540-88436-1_4
    Copyright Statement
    © 2008 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
    Numerical computation and mathematical software
    Medical biochemistry - proteins and peptides (incl. medical proteomics)
    Information and computing sciences
    Publication URI
    http://hdl.handle.net/10072/23639
    Collection
    • Conference outputs

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