文献类型：期刊文献

英文题名：SmartImage: A Machine Learning Method for Nanopore Identifying Chemical Modifications on RNA

作者：Li, Shijia[1];Li, Xinyi[2];Wan, Yong-Jing[1];Ying, Yi-Lun[2,3];Yu, Ru-Jia[2,3];Long, Yi-Tao[2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]Nanjing Univ, Sch Chem & Chem Engn, State Key Lab Analyt Chem Life Sci, 163 Xianlin Rd, Nanjing 210023, Peoples R China;[3]Nanjing Univ, Chem & Biomed Innovat Ctr, 163 Xianlin Rd, Nanjing 210023, Peoples R China

年份：2023

卷号：18

期号：3

外文期刊名：CHEMISTRY-AN ASIAN JOURNAL

收录：;EI(收录号：20230113337880);WOS:【SCI-EXPANDED(收录号:WOS:000906045200001)】；

基金：We would like to thank the funding supported by the National Natural Science Foundation of China (22027806, 21922405, 21834001, and 22090050). Y.-L. Y. is sponsored by the National Ten Thousand Talent Program for young top-notch talent. X. L. is supported by program A for Outstanding Ph.D. candidates of Nanjing University.

语种：英文

外文关键词：biological nanopore; blocked signals; deep neural networks; RNA modifications; single-molecule detection; SmartImage

摘要：RNA modifications modulate essential cellular functions. However, it is challenging to quantitatively identify the differences in RNA modifications. To further improve the single-molecule sensing ability of nanopores, we propose a machine-learning algorithm called SmartImage for identifying and classifying nanopore electrochemical signals based on a combination of improved graph conversion methods and deep neural networks. SmartImage is effective for nearly all ranges of signal duration, which breaks the limitation of the current nanopore algorithm. The overall accuracy (OA) of our proposed recognition strategy exceeded 90% for identifying three types of RNAs. Prediction experiments show that the SmartImage owns the ability to recognize one modified RNA molecule from 1000 normal RNAs with OA >90%. Thus our proposed model and algorithm hold the potential application in clinical applications.