title: Subcellular localization for Gram positive and Gram negative bacterial proteins using linear interpolation smoothing model.
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.
authors.affiliation: University of the South Pacific, Fiji. Electronic address: saini_h@usp.ac.fj.
authors.forename: Harsh
authors.initials: H
authors.lastname: Saini
authors.affiliation: University of the South Pacific, Fiji. Electronic address: raicar_g@usp.ac.fj.
authors.forename: Gaurav
authors.initials: G
authors.lastname: Raicar
authors.affiliation: Griffith University, Australia. Electronic address: abdollah.dehzangi@griffithuni.edu.au.
authors.forename: Abdollah
authors.initials: A
authors.lastname: Dehzangi
authors.affiliation: University of the South Pacific, Fiji. Electronic address: lal_s@usp.ac.fj.
authors.forename: Sunil
authors.initials: S
authors.lastname: Lal
authors.affiliation: University of the South Pacific, Fiji; Griffith University, Australia. Electronic address: alok.fj@gmail.com.
authors.forename: Alok
authors.initials: A
authors.lastname: Sharma
mesh_terms: D000465:Algorithms; D001426:Bacterial Proteins / Q000378:metabolism*; D006090:Gram-Negative Bacteria / Q000378:metabolism*; D006094:Gram-Positive Bacteria / Q000378:metabolism*; D008390:Markov Chains; D015233:Models, Statistical; D009323:Natural Language Processing; D040901:Proteomics / Q000379:methods*; D012680:Sensitivity and Specificity; D013347:Subcellular Fractions / Q000378:metabolism
chemical_list: D001426:Bacterial Proteins
keywords: Dependency models; Feature extraction; Hidden Markov models; Natural language processing