O6-carboxymethyl guanine(O6-CMG) is a highly mutagenic alkylation product of DNA that causes gastrointestinal cancer in organisms. Existing studies used mutant Mycobacterium smegmatis porin A (MspA) nanopore assisted by Phi29 DNA polymerase to localize it. Recently, machine learning technology has been widely used in the analysis of nanopore sequencing data. But the machine learning always need a large number of data labels that have brought extra work burden to researchers, which greatly affects its practicability. Accordingly, this paper proposes a nano-Unsupervised-Deep-Learning method (nano-UDL) based on an unsupervised clustering algorithm to identify methylation events in nanopore data automatically. Specially, nano-UDL first uses the deep AutoEncoder to extract features from the nanopore dataset and then applies the MeanShift clustering algorithm to classify data. Besides, nano-UDL can extract the optimal features for clustering by joint optimizing the clustering loss and reconstruction loss. Experimental results demonstrate that nano-UDL has relatively accurate recognition accuracy on the O6-CMG dataset and can accurately identify all sequence segments containing O6-CMG. In order to further verify the robustness of nano-UDL, hyperparameter sensitivity verification and ablation experiments were carried out in this paper. Using machine learning to analyze nanopore data can effectively reduce the additional cost of manual data analysis, which is significant for many biological studies, including genome sequencing.
Blood velocity inversion based on magnetoelectric effect is helpful for the development of daily monitoring of vascular stenosis, but the accuracy of blood velocity inversion and imaging resolution still need to be improved. Therefore, a convolutional neural network (CNN) based inversion imaging method for intravascular blood flow velocity was proposed in this paper. Firstly, unsupervised learning CNN is constructed to extract weight matrix representation information to preprocess voltage data. Then the preprocessing results are input to supervised learning CNN, and the blood flow velocity value is output by nonlinear mapping. Finally, angiographic images are obtained. In this paper, the validity of the proposed method is verified by constructing data set. The results show that the correlation coefficients of blood velocity inversion in vessel location and stenosis test are 0.884 4 and 0.972 1, respectively. The above research shows that the proposed method can effectively reduce the information loss during the inversion process and improve the inversion accuracy and imaging resolution, which is expected to assist clinical diagnosis.