文献类型：期刊文献

英文题名：Aerolysin Nanopore Identification of Single Nucleotides Using the AdaBoost Model

作者：Sui, Xue-Jie[1];Li, Meng-Yin[2];Ying, Yi-Lun[2];Yan, Bing-Yong[1];Wang, Hui-Feng[1];Zhou, Jia-Le[1];Gu, Zhen[2];Long, Yi-Tao[2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Chem & Mol Engn, Shanghai 200237, Peoples R China

年份：2019

卷号：3

期号：2

起止页码：134

外文期刊名：JOURNAL OF ANALYSIS AND TESTING

收录：EI(收录号：20214311068174);WOS:【ESCI(收录号:WOS:000472905400003)】；

基金：This research was supported by the National Natural Science Foundation of China (6187118, 2183400 and 21711530216), the "Chen Guang" project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation (17CG27).

语种：英文

外文关键词：Nanopore single nucleotide discrimination; Single molecule analysis; AdaBoost; Hidden Markov model

摘要：Nanopores employ the ionic current from the single molecule blockage to identify the structure, conformation, chemical groups and charges of a single molecule. Despite the tremendous development in designing sensitive pore-forming materials, at some extent, the analyte with the single group difference still exhibits similar residual current or duration time. The serious overlap in the statistical results of residual current and duration time brings the difficulties in the nanopore discrimination of each single molecules from the mixture. In this paper, we present the AdaBoost-based machine learning model to identify the multiple analyte with single group difference in the mixed blockages. A set of feature vectors which is obtained from Hidden Markov Model (HMM) is used to train the AdaBoost model. By employing the aerolysin sensing of 5'-AAAA-3' (AA(3)) and 5'-GAAA-3' (GA(3)) as the model system, our results show that AdaBoost model increases the identification accuracy from similar to 0.293 to above 0.991. Furthermore, five sets of mixed blockages of AA(3) and GA(3 )further validate the average accuracy of training and validation, which are 0.997 and 0.989, respectively. The proposed methods improve the capacity of wild-type biological nanopore in efficiently identify the single nucleotide difference without designing of protein and optimizing of the experimental condition. Therefore, the AdaBoost-based machine learning approach could promote the nanopore practical application such as genetic and epigenetic detection.