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

Nasibov E., Kandemir Çavaş Ç.

COMPUTATIONAL BIOLOGY AND CHEMISTRY, cilt.32, sa.6, ss.448-451, 2008 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 32 Sayı: 6
  • Basım Tarihi: 2008
  • Doi Numarası: 10.1016/j.compbiolchem.2008.07.011
  • Dergi Adı: COMPUTATIONAL BIOLOGY AND CHEMISTRY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.448-451
  • Anahtar Kelimeler: Amino acid composition, Jackknife test, Optimally weighted fuzzy k-nearest neighbor, Subcellular location, FUNCTIONAL DOMAIN COMPOSITION, AMINO-ACID-COMPOSITION
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

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. (C) 2008 Elsevier Ltd. All rights reserved.