Format

Send to

Choose Destination
J Theor Biol. 2015 Dec 7;386:25-33. doi: 10.1016/j.jtbi.2015.08.020. Epub 2015 Sep 18.

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

Author information

1
University of the South Pacific, Fiji. Electronic address: saini_h@usp.ac.fj.
2
University of the South Pacific, Fiji. Electronic address: raicar_g@usp.ac.fj.
3
Griffith University, Australia. Electronic address: abdollah.dehzangi@griffithuni.edu.au.
4
University of the South Pacific, Fiji. Electronic address: lal_s@usp.ac.fj.
5
University of the South Pacific, Fiji; Griffith University, Australia. Electronic address: alok.fj@gmail.com.

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 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.

KEYWORDS:

Dependency models; Feature extraction; Hidden Markov models; Natural language processing

PMID:
26386142
DOI:
10.1016/j.jtbi.2015.08.020
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center