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

Authors:
Efendi Nasibov;Cagin Kandemir-Cavas
Affiliations:
Dokuz Eylül University, Faculty of Arts and Sciences, Department of Statistics, 35160 Kaynaklar Campus, Izmir, Turkey;Dokuz Eylül University, Faculty of Arts and Sciences, Department of Statistics, 35160 Kaynaklar Campus, Izmir, Turkey
Venue:
Computational Biology and Chemistry
Year:
2008

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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.