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Identification of m6A site based on reduction ofnucleotide physical-chemical properties significance(PDF)


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Identification of m6A site based on reduction ofnucleotide physical-chemical properties significance
Zhang Ming12Xu Yan1Chen Tao1Wang Changbao1Yu Dongjun2
1.School of Computer Science and Engineering,Jiangsu University of Science and Technology,Zhenjiang 212003,China; 2.School of Computer Science and Engineering,Nanjing University of Scienceand Technology,Nanjing 210094,China
heuristic reduction algorithm classifier ensemble pseudo dinucleotide composition auto variance and cross covariance
In order to improve the prediction accuracy of N6-methyladenosine(m6A)sites on the ribonucleic acid(RNA)sequence,a novel K-fold selection heuristic reduction algorithm is developed here based on the significance measurement of the nucleotide physical-chemical properties.The final predictor is constructed with the classifier ensemble,the K base classifiers are obtained by combining the support vector machine(SVM)with reduction subsets,and the reduction algorithm is used to remove the redundant physical-chemical properties and re-encode the RNA sample sequence. Finally,the jackknife test on the benchmark dataset verifies the pseudo dinucleotide composition analysis and auto variance and cross covariance transformation respectively,which are two typical representative algorithms based on nucleotide physical-chemical properties. The experimental results show that the proposed algorithm can significantly improve the overall prediction performance of m6A sites.


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Last Update: 2019-04-26