Prediction of cancer drug response based on deep learning methods has become the basis for personalized medicine. A large amount of multi-omics data on human cell lines and anti-cancer drugs are available for this task. Feature extraction from multi-omics data is essential for this task. Driver mutations affect key signaling pathways, which can significantly alter the sensitivity of tumor cells to drugs and affect the prediction of drug response. However, the contribution of cancer driver mutations to model performance has not been fully investigated. We propose AFMCDR, a novel approach that addresses key challenges in predicting cancer drug response. AFMCDR innovatively utilizes multi-omics data on driver mutations in cell lines and multidimensional drug data, filling a gap in understanding how these data affect drug response mechanisms, a key research area; we propose a hierarchical feature enhancement network that captures both the histological feature level and deep associations at the biological entity level; and we propose a biomedical crossnetwork attention mechanism to capture associations between biomedical entities. In addition, we design a representation learning method that combines drug responses with biomedical information. Finally, we propose a biological network-based adaptive fusion mechanism for dynamically optimizing the fusion of multi-omics features. Experiments on two benchmark datasets show that AFMCDR outperforms existing methods in predicting cancer drug responses. Visualization analysis shows that driver mutation features significantly improves the performance of drug response prediction. These findings confirm that AFMCDR is a powerful tool for predicting cancer drug response. The source code and datasets of AFMCDR are available at https://anonymous.4open.science/r/AFMCDR-DA65/.

Accurate prediction of anti-cancer drug response requires simultaneous assessment of both categorical sensitivity and Individual Treatment Effects (ITE). Current computational approaches predominantly focus on correlations rather than causal mechanisms. We present HyperCausal, a novel multi-task framework integrating hypergraph representation learning with causal inference for robust drug response prediction and personalized treatment selection. HyperCausal constructs bipartite graphs and hypergraphs to capture complex biological relationships. The framework explicitly models network inter-ference effects through hypergraph convolutional networks. To address confounding bias, we employ representation balancing based on Wasserstein distance for robust causal effect estimation. Evaluation on GDSC and CCLE datasets achieves state-of-the-art performance. The framework reaches AUC of 0.9074 and AP of 0.9073 on GDSC dataset. Comprehensive ablation studies confirm the criticality of causal inference components. EGFR case studies demonstrate clinical relevance through correct stratification based on mutation status. This establishes a principled approach for computational drug discovery with mechanistic understanding. Code and data are available https://anonymous.4open.science/r/HyperCausal-2436 for reproducibility.

Drug response prediction (DRP) is crucial for precision oncology. Current models exhibit poor generalization from preclinical cell lines to patient tumors due to domain shift. While unsupervised domain adaptation offers a solution, existing frameworks suffer from two training instabilities. These include gradient conflicts between competing objectives and prototype instability from high-variance cancer subtype representations. These fundamental issues prevent reliable identification of transferable biomarkers essential for clinical decision-making. We propose SAGA-DRP, a framework with dual stabilization mechanisms grounded in cancer biology. Our gradient-guided annealing identifies parameter regions corresponding to conserved biological pathways. This resolves destructive interference between objectives. Momentum-smoothed prototype updates stabilize molecular signatures of cancer subtypes. This ensures consistent drug response pattern capture across domains. SAGA-DRP represents the first framework to simultaneously address both stability challenges through biologically-motivated solutions. Evaluations on TCGA and PDTC datasets across nine FDA-approved drugs demonstrate significant improvements. SAGA-DRP achieves superior accuracy with AUC of 0.6851, representing a 1.9% improvement. The framework also shows enhanced stability with 73% prototype variance reduction. This provides a robust foundation for clinical translation in precision oncology. Statistical significance testing confirms the reliability of improvements. Pathway analysis validates that identified biomarkers align with known cancer biology. To ensure reproducibility, data and code are accessible via anonymous links at https://anonymous.4open.science/r/SAGA-DRP-34CC.

The accurate prediction of genedisease associations is essential for understanding pathogenic mechanisms and identifying therapeutic targets. While existing computational methods have integrated diverse biological information, they struggle with the sparsity of known associations and the lack of reliable negative samples. To address these issues, we propose NCVGAE, a novel framework that extends the Variational Graph Auto-Encoder (VGAE) by incorporating a hybrid negative sampling strategy and contrastive learning to enhance representation learning under sparse supervision. We devise a Hybrid Negative Sampling strategy that combines similarity-guided selection-using topological and biological similarity to identify high-confidence negatives-with random sampling to ensure diversity. This helps reduce noise from unreliable negatives and improves the quality of supervision. The encoder module employs a Graph Neural Network (GNN) to effectively learn robust embeddings for genes and diseases. During the training phase, a contrastive learning objective refines embeddings by pulling together true associations and pushing apart negatives. A multilayer perceptron then predicts association scores. Experiments on three public datasets show that NCVGAE consistently outperforms state-of-the-art methods in terms of AUC and AUPR, highlighting its effectiveness and generalizability.

Predicting gene-disease associations is essential for understanding disease pathogenesis and determining therapeutic targets. While prior methods have integrated diverse biological information to make predictions, they still encounter several challenges. First, incomplete and sparse gene-disease association data constrain model performance. Second, integrating heterogeneous data sources is not straightforward. To address these challenges, we propose a novel method, DAVGAE, which combines data augmentation, Variational Graph Auto-Encoders (VGAE), and attention mechanisms. DAVGAE integrates both the biological and topological features of genes and diseases to address challenges such as data sparsity and heterogeneity. By leveraging these features, it calculates cosine similarity scores for gene-disease pairs and applies a novel data augmentation strategy to enhance association data by selecting gene-disease associations with higher similarity scores. Using a four-layer Graph Neural Network (GNN) encoder, DAVGAE effectively learns robust and discriminative representations for genes and diseases within the association network. Finally, an inner product decoder predicts association scores for all gene-disease pairs. Comprehensive experiments on three gene-disease association datasets reveal that DAVGAE outperforms baseline models in predicting gene-disease associations.

Prediction of disease-drug interactions(DDIs) plays a vital role in drug development in various areas, such as precision medicine, drug development and overcoming the issue of drug resistance. Despite continuous advancements in methods for predicting disease-drug interactions(DDIs) and the achievement of promising results, current approaches are still limited by issues related to the homogeneity of data and the consistency of vector representations. Firstly, DDI prediction should consider a variety of heterogeneous relationships, such as drug-protein associations and drug-induced side effects in patients. These diverse relationships can help uncover potential associations between diseases and drugs. Secondly, neglecting the issue of inconsistency in vector representations within heterogeneous network embeddings may affect the accuracy of capturing relationships and similarities between entities. Here, we develop KGE GCLR, a framework for DDIs prediction by combining knowledge graph(KG) and graph contrastive learning for recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then under the paradigm of graph contrastive learning provided to integrate heterogeneous auxiliary information into the recommendation system(GCLR). The KGE GCLR was evaluated in realistic scenarios, and achieved accurate and robust predictions on two benchmark datasets. Our results indicate that the KGE GCLR, by addressing the inconsistency challenges in embedding heterogeneous networks, provides valuable insights for integrating knowledge graph data and recommendation system-based techniques into a framework, thereby enhancing the predictive capabilities for disease-drug association discovery.

Since microRNA (miRNA) can participate in the post-transcriptional regulation of gene expression, they can become potential markers of diseases, therapeutic targets and regulatory factors of disease development. So it is vital to predict miRNA-disease association(MDA). However, recent researches lack feature learning based on diverse views, and pay little attention to the sequence features of miRNA. In this study, we extract the initial feature from miRNA sequences and build homogeneous graph and heterogeneous graph for miRNA-disease association. To effectively learn low-dimensional representations of miRNA and disease features from different perspectives, we propose a Hybrid Graph Autoencoder model (HGAMDA). The model learn the embedding of nodes from multiple homogeneous graphs separately, utilizing graph-level attention mechanisms to learn the importance of different homogeneous graphs. Then, it further learns low-dimensional features of nodes in the heterogeneous graph. The experimental results show that the AUC value of HGAMDA in 5-fold cross-validation is 0.9878 on HMDD v4.0 dataset, significantly outperforming other baseline methods.

Clinical studies have shown that microbes in the human body are closely related to human health. Microbes can influence the activity and toxicity of drugs. So they play an important role in the treatment of diseases. It is critical to research the associations between drugs and microbes for drug development and precision medicine. Recently, there are several computation methods for predicting drug-related microbes. However, these methods ignore the information of diseases because diseases are the bridge between drugs and microbes. Here we introduce a new model (called MDMD) proposed to predict drug-related microbes based on the Metapaths from a heterogeneous network constructed by using the data of Diseases, Microbes, Drugs, the associations of microbe-disease and disease-drug. The MDMD uses an aggregation of the metapath features that can effectively abundance the embedding of the features for different types of nodes and edges in the heterogeneous networks. Then, the MDMD uses the attention mechanism to mark the importance of the metapath vector for each node type which can improve the quality of feature embedding. Experimental results demonstrate that the MDMD improves accuracy by 1.9% compared with other models. The MDMD is also used to predict the microbes of two drugs Lamivudine and Tenofovir which are the antiretroviral drugs used to treat the Acquired Immune Deficiency Syndrome(AIDS). The results show that 90-95% of microbes are reported in the PubMed. In addition, we found that lamivudine may be useful for the treatment of tuberculosis caused by Mycobacterium tuberculosis (Mtb). An online platform of the MDMD is available in https://mdmd2023.bit1024.top/, in which the source code of the MDMD and the data in the work can be downloaded.

Aspect-based Sentiment Triplet Extraction (ASTE) is an emerging research area in sentiment analysis, which aims to identify aspect terms and their corresponding sentiment polarity and opinion terms in sentences. Previous studies have tried to use graph neural networks on dependency trees to handle ASTE tasks through pipelines or end-to-end approaches, but these methods have limitations and do not effectively combine semantic and syntactic information as well as various features in span markers.To address these issues, we propose a new solution called Enhanced Syntax and Semantics Dual-Branch Graph Convolutional Network (ESSDB-GCN), which can better integrate syntactic and semantic information. Specifically, we first designed a syntactic dependency enhancement channel to enhance syntactic features, and optimized syntactic information by treating the syntactic dependency probability matrix as a graph structure. Next, we designed a semantic channel with a self-attention mechanism to enhance semantic information and proposed orthogonal and differential regularizers to strengthen semantic relevance. Lastly, we explored span-level information and constraints to solve the problem of emotion word tagging, in order to generate more accurate aspect-based sentiment triplets. Our proposed ESSDB-GCN demonstrates strong performance on multiple benchmark datasets, proving the effectiveness of our method.