Prediction of nuclear proteins is one of the major challenges in genome annotation. A method, NcPred is described, for predicting nuclear proteins with higher accuracy exploiting n∈-∈mer statistics with different classification algorithms namely Alternating Decision (AD) Tree, Best First (BF) Tree, Random Tree and Adaptive (Ada) Boost. On BaCello dataset [1], NcPred improves about 20% accuracy with Random Tree and about 10% sensitivity with Ada Boost for Animal proteins compared to existing techniques. It also increases the accuracy of Fungal protein prediction by 20% and recall by 4% with AD Tree. In case of Human protein, the accuracy is improved by about 25% and sensitivity about 10% with BF Tree. Performance analysis of NcPred clearly demonstrates its suitability over the contemporary in-silico nuclear protein classification research. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Islam, M. S., Kabir, A., Sakib, K., & Hossain, M. A. (2011). NcPred for accurate nuclear protein prediction using n-mer statistics with various classification algorithms. In Advances in Intelligent and Soft Computing (Vol. 93, pp. 285–292). https://doi.org/10.1007/978-3-642-19914-1_38
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