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Subcellular localization for Gram Positive and Gram Negative Bacterial Proteins using Linear Interpolation Smoothing Model

Saini, Harsh and Raicar, Gaurav and Lal, Sunil P. and Dehzangi, A. and Sharma, Alokanand (2015) Subcellular localization for Gram Positive and Gram Negative Bacterial Proteins using Linear Interpolation Smoothing Model. Journal of Theoretical Biology, 386 . pp. 25-33. ISSN 0022-5193

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    Abstract

    Protein subcellular localization is an important topic in proteomics since it is related to a proteins overall function, help 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 hinder 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.

    Item Type: Journal Article
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    T Technology > T Technology (General)
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Faculty of Science, Technology and Environment (FSTE)
    Depositing User: Harsh Saini
    Date Deposited: 20 Oct 2015 16:10
    Last Modified: 23 May 2016 09:09
    URI: http://repository.usp.ac.fj/id/eprint/8423
    UNSPECIFIED

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