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  • Subcellular localization for Gram positive and Gram negative bacterial proteins using linear interpolation smoothing model

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    DehzangiPUB532.pdf (441.6Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Saini, Harsh
    Raicar, Gaurav
    Dehzangi, Abdollah
    Lal, Sunil
    Sharma, Alok
    Griffith University Author(s)
    Sharma, Alok
    Dehzangi, Iman
    Year published
    2015
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    Abstract
    Protein subcellular localization is an important topic in proteomics since it is related to a protein׳s overall function, helps in the understanding of metabolic pathways, and in drug design and discovery. In this paper, a basic approximation technique from natural language processing called the linear interpolation smoothing model is applied for predicting protein subcellular localizations. The proposed approach extracts features from syntactical information in protein sequences to build probabilistic profiles using dependency models, which are used in linear interpolation to determine how likely is a sequence to belong to ...
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    Protein subcellular localization is an important topic in proteomics since it is related to a protein׳s overall function, helps in the understanding of metabolic pathways, and in drug design and discovery. In this paper, a basic approximation technique from natural language processing called the linear interpolation smoothing model is applied for predicting protein subcellular localizations. The proposed approach extracts features from syntactical information in protein sequences to build probabilistic profiles using dependency models, which are used in linear interpolation to determine how likely is a sequence to belong to a particular subcellular location. This technique builds a statistical model based on maximum likelihood. It is able to deal effectively with high dimensionality that hinders other traditional classifiers such as Support Vector Machines or k-Nearest Neighbours without sacrificing performance. This approach has been evaluated by predicting subcellular localizations of Gram positive and Gram negative bacterial proteins.
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    Journal Title
    Journal of Theoretical Biology
    Volume
    386
    Issue
    33
    DOI
    https://doi.org/10.1016/j.jtbi.2015.08.020
    Copyright Statement
    © 2015 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Mathematical sciences
    Biological sciences
    Microbiology not elsewhere classified
    Information and computing sciences
    Publication URI
    http://hdl.handle.net/10072/101470
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    • Journal articles

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