NcPred for accurate nuclear protein prediction using n-mer statistics with various classification algorithms

Md. Saiful Islam, Alaol Kabir, Kazi Sakib, Alamgir Hossain

    Research output: Contribution to journalArticlepeer-review

    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.
    Original languageEnglish
    Pages (from-to)285-292
    JournalAdvances in Intelligent and Soft Computing
    Volume93
    DOIs
    Publication statusPublished - 2011

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