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Abstract Circular RNA (CircRNA) is a type of non-coding RNAs in which both ends are covalently linked. Researchers have demonstrated that many circRNAs can act as biomarkers of diseases. However, traditional experimental methods for circRNA-disease associations identification are labor-intensive. In this work, we propose a novel method based on the heterogeneous graph neural network and metapaths for circRNA-disease associations prediction termed as HMCDA. First, a heterogeneous graph consisting of circRNA-disease associations, circRNA-miRNA associations, miRNA-disease associations and disease-disease associations are constructed. Then, six metapaths are defined and generated according to the biomedical pathways. Afterwards, the entity content transformation, intra-metapath and inter-metapath aggregation are implemented to learn the embeddings of circRNA and disease entities. Finally, the learned embeddings are used to predict novel circRNA-disase associations. In particular, the result of extensive experiments demonstrates that HMCDA outperforms four state-of-the-art models in fivefold cross validation. In addition, our case study indicates that HMCDA has the ability to identify novel circRNA-disease associations.

Capturing long-term dependency from historical behaviors is the key to the success of sequential recommendation; however, existing methods focus on extracting global sequential information while neglecting to obtain deep representations from subsequences. Previous research has revealed that the restricted inter-item transfer is fundamental to sequential modeling, and some potential substructures of sequences can help models learn more effective long-term dependency compared to the whole sequence. To automatically find better subsequences and perform efficient learning, we propose a sequential recommendation model with a gated recurrent unit and Transformers, abbreviated as GAT4Rec, which employs Transformers with shared parameters across layers to model users’ historical interaction sequences. The representation learned by the gated recurrent unit is used as the gating signal to identify the optimal substructure in user sequences. The fused representation of the subsequence and edge information is extracted by the encoding layer to make the corresponding recommendations. Experimental results on four well-known publicly available datasets demonstrate that our GAT4Rec model outperforms other recommendation models, achieving performance improvements of 5.77%, 1.35%, 11.58%, and 1.79% in the normalized discounted cumulative gain metric (NDCG@10), respectively.

Generative Adversarial Networks (GANs) are a powerful subclass of generative models. Yet, how to effectively train them to reach Nash equilibrium is a challenge. A number of experiments have indicated that one possible solution is to bound the function space of the discriminator. In practice, when optimizing the standard loss function without limiting the discriminator's output, the discriminator may suffer from lack of convergence. To be able to reach the Nash equilibrium in a faster way during training and obtain better generative data, we propose constrained generative adversarial networks, GAN-C, where a constraint on the discriminator's output is introduced. We theoretically prove that our proposed loss function shares the same Nash equilibrium as the standard one, and our experiments on mixture of Gaussians, MNIST, CIFAR-10, STL-10, FFHQ, and CAT datasets show that our loss function can better stabilize training and yield even better high-quality images.

In order to facilitate designers to explore the market demand trend of laptops and to establish a better “network users-market feedback mechanism”, we propose a design and research method of a short text mining tool based on the K-means clustering algorithm and Kano mode. An improved short text clustering algorithm is used to extract the design elements of laptops. Based on the traditional questionnaire, we extract the user’s attention factors, score the emotional tendency, and analyze the user’s needs based on the Kano model. Then, we select 10 laptops, process them by the improved algorithm, cluster the evaluation words and quantify the emotional orientation matching. Based on the obtained data, we design a visual interaction logic and usability test. These prove that the proposed method is feasible and effective.