文献类型：期刊文献

英文题名：Intelligent identification of multi-level nanopore signatures for accurate detection of cancer biomarkers

作者：Zhang, Jian-Hua[1];Liu, Xiu-Ling[1];Hu, Zheng-Li[2,3];Ying, Yi-Lun[2,3];Long, Yi-Tao[2,3]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Adv Mat, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Sch Chem & Mol Engn, Shanghai 200237, Peoples R China

年份：2017

卷号：53

期号：73

起止页码：10176

外文期刊名：CHEMICAL COMMUNICATIONS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000410360700019)】；

基金：This work was supported in part by the National Natural Science Foundation of China (Grant No. 21505043, 21421004 and 21327807) and the Fundamental Research Funds for the Central Universities (Grant No. 222201718001, 222201717003, and 222201714012). Y.-L. Ying is supported by the Chinese Post-Doctor Fund (2016T90340). The authors would like to thank Dr Qihui Du, People's Hospital of Nanshan District, Shenzhen, China, for providing the serum sample.

语种：英文

摘要：To achieve accurate detection of cancer biomarkers with nanopore sensors, the precise recognition of multi-level current blockage events (signature) is a pivotal problem. However, it remains rather a challenge to identify the multi-level current blockages of target biomarkers in nanopore experiments, especially for the nanopore analysis of serum samples. In this work, we combined a modified DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm with the Viterbi training algorithm of the hidden Markov model (HMM) to achieve intelligent retrieval of multi-level current signatures from microRNA in serum samples. The results showed that the developed intelligent data analysis method is highly efficient for processing the large-scale nanopore data, which facilitates future application of nanopores to the clinical detection of cancer biomarkers.