The rapid advancement of medical dialog generation (MDG) techniques has enabled medical dialog systems (MDSs) to generate high-quality responses rich in medical expertise by integrating diverse medical information. However, they still encounter several challenges, including generic response generation, lack of semantic precision, and imprecise dialog topic extraction. This study aims to design a novel model to address these challenges simultaneously. Correspondingly, we propose the TRL-HMIE model, which represents transformer reinforcement learning (RL) for heterogeneous medical information extraction. In particular, we incorporate GPT-3 from transformer-related models as the reference language model. Our enhancements focused on three key aspects. First, we developed a conversation-topic classifier to precisely categorize conversation topics, supporting the conversation-topic locator module in generating reliable conversation topics. Second, the model employs a multihead attention mechanism to capture crucial information from the dialog context, facilitating the extraction of key dialog information and enhancing the accuracy of heterogeneous information extraction. Finally, the model integrates RL and a reward fusion mechanism, which, combined with its ability to handle multisource information and long dialog contexts, generates optimized rewards for the TRL-HMIE model, encouraging the production of doctor responses with precise semantics and dialog topics. The experimental results demonstrate that the proposed method achieves a 6.07% improvement over the benchmark model on the MedDG and MedDialog datasets. The experimental results demonstrate that the proposed method achieves a 6.07% improvement over the benchmark model on the MedDG and MedDialog datasets.

The goal of community search is to provide effective solutions for real-time, high-quality community searches within large networks. In many practical applications, such as event organization and friend recommendations, discovering various community structures within a network is crucial for users. However, existing community search algorithms rarely address issues within temporal graphs, and those that do often have two main limitations: (1) traditional community search methods become inefficient and experience significant increases in computation time when scaled to large graphs; (2) while GNN-based community search methods for temporal graphs offer generalizability, they often focus solely on community connectivity and lack cohesiveness. Therefore, we propose a new model PK-GCN, based on Graph Neural Networks (GNNs), to identify persistent k-core communities in temporal networks. This model can handle dynamic changes in temporal graphs and identify communities that persist over time. Compared to existing community search methods, our model not only finds communities with tighter structures but also allows for dynamic queries based on user input without needing retraining. Specifically, our model constructs features by integrating k-core information from core decomposition, graph features, and query features, resulting in more expressive node representations. Additionally, we designed a flexible dynamic query mechanism that allows users to input time information to query communities. Experiments on multiple datasets demonstrate that our model outperforms other GNN-based community search algorithms in F1-score.

Comprehensive analysis of multi-omics data has now garnered significant attention. However, due to the diversity of multi-omics data, integrating multi-omics information presents a formidable challenge for researchers. Moreover, the high dimensionality and sparsity characteristics in omics data further complicate multi-omics data analysis. To address these challenges and obtain high-quality representations suitable for downstream tasks, we propose a self-supervised clustering learning framework called the Multi-view Feature Hierarchical Contrastive Clustering model (MFHCC) to extract multi-level features. Firstly, the proposed model considers multi-omics as multi-modality and employs an autoencoder for each modality to integrate diverse omics information simultaneously. Secondly, it utilizes a multilevel feature extraction framework with contrastive learning methods to mitigate the impact of redundant information and null values on representation quality while capturing semantic information embedded in the data. Additionally, the model incorporates a deep clustering module to guide the representation toward downstream tasks while integrating high-level features for guidance. Through extensive experiments conducted on pan-cancer datasets, we validate the effectiveness of MFHCC. For instance, the model achieves an accuracy exceeding 76% by omics types, thus confirming its superior performance.

De novo drug design is a challenging task that involves understanding the principles of chemistry, chemical properties, and the rules that govern molecular interactions. Deep learning-based generative models, such as MolGAN, offer a promising approach for generating new molecules with the desired chemical properties from molecular graphs. Such models often combine a discrete generative adversarial network (GAN) and reinforcement learning (RL) to produce highly valid and novel molecules. However, the severe mode collapse problem leads to low performance. This study aims to alleviate and investigate the effect of multiple factors on mode collapse. We conducted experiments on different sampling methods, training epochs, and datasets of various volumes and evaluated the experimental results using performance metrics such as validity, uniqueness, novelty, and diversity. The experimental results demonstrate that noise sampling distributions, training epochs, and training data volumes affect performance. The experimental results provide a direction for mitigating the mode collapse problem for RL-based discrete GANs.

Accumulating evidence has demonstrated that RNAs play an important role in identifying various complex human diseases. However, the number of known disease related RNAs is still small and many biological experiments are time-consuming and labor-intensive. Therefore, researchers have focused on developing useful computational algorithms to predict associations between diseases and RNAs. It is useful for people to identify complex human diseases at molecular level, especially in diseases diagnosis, therapy, prognosis and monitoring. In this paper, we propose a novel framework Graph Convolutional Attention Network(GCAN) to predict potential disease-RNAs associations. Facing thousands of associations, GCAN benefits from the efficiency of deep learning model. Compared to other disease-RNAs association prediction methods, GCAN operates the computation process from global structure of disease-RNAs network with graph convolution networks(GCN) and can also integrate local neighborhoods with the attention mechanism. What is more, GCAN is at the first attempt to utilize GCN to discover the feature representation of the latent nodes in disease-RNAs network. In order to evaluate the performance of GCAN, we conduct experiments on two different disease-RNAs networks: disease-miRNA and disease-lncRNA. Comparisons of several state-of-the-art methods using disease-RNAs networks show that our novel frameworks outperform baselines by a wide margin in potential disease-RNAs associations.