Prediction of nuclear proteins using SVM and HMM models
Open Access
- 19 January 2009
- journal article
- Published by Springer Nature in BMC Bioinformatics
- Vol. 10 (1) , 22
Abstract
The nucleus, a highly organized organelle, plays important role in cellular homeostasis. The nuclear proteins are crucial for chromosomal maintenance/segregation, gene expression, RNA processing/export, and many other processes. Several methods have been developed for predicting the nuclear proteins in the past. The aim of the present study is to develop a new method for predicting nuclear proteins with higher accuracy.Keywords
This publication has 18 references indexed in Scilit:
- NucPred—Predicting nuclear localization of proteinsBioinformatics, 2007
- BaCelLo: a balanced subcellular localization predictorBioinformatics, 2006
- Prediction of Mitochondrial Proteins Using Support Vector Machine and Hidden Markov ModelJournal of Biological Chemistry, 2006
- Mimicking Cellular Sorting Improves Prediction of Subcellular LocalizationJournal of Molecular Biology, 2005
- Support Vector Machine-based Method for Subcellular Localization of Human Proteins Using Amino Acid Compositions, Their Order, and Similarity SearchJournal of Biological Chemistry, 2005
- ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLASTNucleic Acids Research, 2004
- MITOPRED: a genome-scale method for prediction of nucleus-encoded mitochondrial proteinsBioinformatics, 2004
- Finding nuclear localization signalsEMBO Reports, 2000
- Predicting Subcellular Localization of Proteins Based on their N-terminal Amino Acid SequenceJournal of Molecular Biology, 2000
- Assessing the accuracy of prediction algorithms for classification: an overviewBioinformatics, 2000