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- W1869871206 abstract "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." @default.
- W1869871206 created "2016-06-24" @default.
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- W1869871206 date "2011-01-01" @default.
- W1869871206 modified "2023-10-14" @default.
- W1869871206 title "NcPred for Accurate Nuclear Protein Prediction Using n-mer Statistics with Various Classification Algorithms" @default.
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- W1869871206 doi "https://doi.org/10.1007/978-3-642-19914-1_38" @default.
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