©1996-2019 All Rights Reserved. Online Journal of Bioinformatics. You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international copyright treaties. Permission to use any of these pages in any other way besides the before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety to OJB publications. This article may be copied once but may not be reproduced or re-transmitted without the express permission of the editors.


Online Journal of Bioinformatics
  Volume 6 : 8-21, 2005.

A novel method to predict protein subcellular localizations

          Huang J,  Shi H


School of Computer Science and Mathematics and Statistics, Wuhan University and School of Science, Huazhong Agricultural University, Hubei Province, P.R. China. 




Huang J,  Shi H, A novel method to predict protein subcellular localizations, Onl J Bioinform., 6:8-21, 2004. Amino acid similarity and structure were used in a model to predict subcellular localizations for prokaryotic and eukaryotic proteins. Tested with Reinhart and Hubbard’s dataset, prediction accuracies reached 100% using the self-consistency test, 92% for prokaryotic sequences and 79.2% for eukaryotic protein sequences with the jackknife test. The results suggest that amino acid structure may be a useful predictor of protein subcellular localization.


KEY WORDS: Support vector machine, subcellular location, amino acid similarity.