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Protein subcellular location prediction using optimally weighted fuzzy k-NN algorithm

Paper ID Volume ID Publish Year Pages File Format Full-Text
15370 1407 2008 4 PDF Available
Title
Protein subcellular location prediction using optimally weighted fuzzy k-NN algorithm
Abstract

Optimally weighted fuzzy k-nearest neighbors (OWFKNN) algorithm has been used to predict proteins’ subcellular locations based on their amino acid composition, in this paper. The datasets used consists of two species which are 997 prokaryotic and 2427 eukaryotic protein sequences. The overall prediction accuracy achieved is about 88.5% for prokaryotic sequences and 86.2% for eukaryotic sequences in a jackknife test. Compared to other algorithms developed for the prediction of protein subcellular location, OWFKNN gives very satisfying results. Therefore, OWFKNN can be used as an alternative method to predict protein localization.

Keywords
Amino acid composition; Jackknife test; Optimally weighted fuzzy k-nearest neighbor; Subcellular location
First Page Preview
Protein subcellular location prediction using optimally weighted fuzzy k-NN algorithm
Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 32, Issue 6, December 2008, Pages 448–451
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering