Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains

Background: The subcellular location of a protein is closely related to its function. It would be worthwhile to develop a method to predict the subcellular location for a given protein when only the amino acid sequence of the protein is known. Although many efforts have been made to predict subcellular location from sequence information only, there is the need for further research to improve the accuracy of prediction. Results: A novel method called HensBC is introduced to predict protein subcellular location. HensBC is a recursive algorithm which constructs a hierarchical ensemble of classifiers. The classifiers used are Bayesian classifiers based on Markov chain models. We tested our method on six various datasets; among them are Gram-negative bacteria dataset, data for discriminating outer membrane proteins and apoptosis proteins dataset. We observed that our method can predict the subcellular location with high accuracy. Another advantage of the proposed method is that it can impr...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Alla Bulashevska, Roland Eils
• Keywords: BMCBI 2006, Protein Subcellular Location, Proteins, Subcellular Location