Aims: Driver mutations are crucial factors in the occurrence and development of cancer. Identifying cancer-related driver genes is of great significance for understanding the mechanisms of cancer initiation, prevention, and treatment. With the continuous accumulation of cancer data, how to effectively utilize these data for the identification of cancer driver genes has become a major challenge in the field of cancer biology.

Methods: We propose a novel computational model called iCDG-MOHGAT. This model integrates multi-omics pan-cancer data (such as mutations, DNA methylation, etc.), multi-dimensional gene networks, and disease semantic similarity networks to identify cancer driver genes. We first construct multi-dimensional gene networks using various types of gene correlation information (protein-protein interaction, gene sequence similarity, etc.) and establish disease semantic similarity networks for relevant cancers. Due to the complexity of node and edge types, we utilize a heterogeneous graph attention network to learn and extract features from the multi-dimensional gene networks and disease semantic similarity networks. We also incorporate a fusion learning module to effectively integrate features from different dimensions. Finally, we optimize the random forest classifier using the sparrow algorithm for the task of predicting cancer driver genes.

Results: Experimental results demonstrate that iCDG-MOHGAT outperforms many state-of-the-art models in terms of AUPR and AUROC. In the final prediction results, 91% of the predicted new driver genes have at least one supporting evidence of being cancer genes. In the laboratory, this model can serve as an effective tool for identifying cancer driver genes.

Conclusion: We have introduced a novel computational model named iCDG-MOHGAT, which precisely identifies cancer driver genes by integrating multi-omics pan-cancer data and intricate multidimensional gene networks, coupled with disease semantic similarity networks. Experimental results demonstrate that iCDG-MOHGAT outperforms many state-of-the-art models in terms of AUPR and AUROC. In the final prediction results, 91% of the predicted genes have supporting evidence. In the laboratory, this model can serve as an effective tool for identifying cancer driver genes.

Aims: Drug-target affinity (DTA) prediction is crucial for drug discovery and repositioning. However, existing deep learning-based methods often overlook the synergy between the topological structure of DTA networks and the multimodal features of drugs and targets themselves.

Methods: This study proposes a new method, MIGDTA, a DTA prediction method based on multi-source information and graph convolutional network (GCN), which enhances prediction accuracy by integrating local features with global interaction information. MIGDTA first constructs a drug molecular graph, a target protein graph, and a DTA network, while computing molecular fingerprints and protein descriptors. Subsequently, it employs a graph isomorphism network to learn graph features, a GCN to capture network features, and a multilayer prceptron to encode biological features. Then, it refines heterogeneous network and graph features iteratively through the GCN, and finally concatenates the fused features with biological features for affinity prediction.

Results: Comparative experiments on benchmark datasets demonstrate that MIGDTA significantly outperforms existing methods. On the Davis dataset, compared to the best baseline method, MIGDTA reduces mean squared error (MSE) to 0.185, increases CI by 0.006, and improves $r_m^2$ by 5%. Similar enhancements were observed on the KIBA dataset, where MIGDTA achieves an MSE of 0.130, along with 0.002 and 1% gains in CI and $r_m^2$, respectively.

Conclusion: Feature ablation studies verify the core role of graph features in modeling local structures and network features in capturing global topology, along with the supplementary importance of biological features. Comparative analyses of feature integration approaches confirm the effectiveness of the feature refinement module in fusing multimodal features and enhancing model discriminability.