Zhang Jian-Hua, Liu Xiu-Ling, Hu Zheng-Li, Ying Yi-Lun, Long Yi-Tao
School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China.
Chem Commun (Camb). 2017 Sep 12;53(73):10176-10179. doi: 10.1039/c7cc04745b.
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.
为了利用纳米孔传感器实现对癌症生物标志物的准确检测，精确识别多级电流阻断事件（特征）是一个关键问题。然而，在纳米孔实验中识别目标生物标志物的多级电流阻断仍然是一个相当大的挑战，特别是对于血清样本的纳米孔分析。在这项工作中，我们将改进的DBSCAN（基于密度的带有噪声的空间聚类应用）算法与隐马尔可夫模型（HMM）的维特比训练算法相结合，以实现从血清样本中的微小RNA中智能检索多级电流特征。结果表明，所开发的智能数据分析方法在处理大规模纳米孔数据方面具有很高的效率，这有利于纳米孔在癌症生物标志物临床检测中的未来应用。
