In deep learning， as the depth of Convolutional Neural Network （CNN） increases， more and more data is required for neural network training， but gene structure variation is a small sample event in large-scale genetic data， resulting in a very shortage of image data of variant genes， which seriously affects the training effect of CNN and causes the problems of poor gene structure variation detection precision and high false positive rate. In order to increase the number of gene structure variation samples and improve the precision of CNN to identify gene structure variation， a gene image data augmentation method was proposed based on GAN （Generative Adversarial Network）， namely GeneGAN. Firstly， initial genetic image data was generated by using the Reads stacking method and it was divided into two datasets including variant gene images and non-variant gene images. Secondly， GeneGAN was used to augment the variant image samples to balance the positive and negative datasets. Finally， CNN was used to detect the datasets before and after augmentation， and precision， recall and F1 score were used as measurement indicators. Experimental results show that compared with tradional augmentation method， GAN based augmentation method and feature extraction method， the F1 score of GeneGAN is improved by 1.94 to 17.46 percentage points， verifying that GeneGAN method can improve the precision of CNN to identify gene structure variation.

Social media highly facilitates people’s daily communication and disseminating information， but it is also a breeding ground for rumors. Therefore， how to automatically monitor rumor dissemination in the early stage is of great practical significance， but the existing detection methods fail to take full advantage of the semantic information of the microblog information propagation graph. To solve this problem， based on Heterogeneous graph Attention Network （HAN）， a rumor monitoring model was built， namely MicroBlog-HAN. In the model， a hierarchical attention mechanism including node-level attention and semantic-level attention was adopted. First， the neighbors of microblog nodes were combined by the node-level attention to generate two groups of node embeddings with specific semantics. After that， different semantics were fused by the semantic-level attention to obtain the final node embeddings of microblog， which were then treated as the classifier’s input to perform the binary classification task. In the end， the classification result of whether the input microblog is rumor or not was given. Experimental results on two real-world microblog rumor datasets convincingly prove that MicroBlog-HAN model can accurately identify microblog rumors with an accuracy over 87%.