TY - JOUR
T1 - NcPred for accurate nuclear protein prediction using n-mer statistics with various classification algorithms
AU - Islam, Md. Saiful
AU - Kabir, Alaol
AU - Sakib, Kazi
AU - Hossain, Alamgir
N1 - 5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011) Salamanca, Spain 6-8 April 2011.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-642-19914-1_38
DO - 10.1007/978-3-642-19914-1_38
M3 - Article
VL - 93
SP - 285
EP - 292
JO - Advances in Intelligent and Soft Computing
JF - Advances in Intelligent and Soft Computing
SN - 2194-5357
ER -