Neuropeptides are crucial signaling molecules that regulate diverse physiological processes spanning growth, social behavior, learning, memory, metabolism, homeostasis, reproduction, and neural differentiation across both nervous and peripheral systems. Dysregulation of neuropeptides signaling is closely linked to various pathological conditions, such as neurological disorders, metabolic diseases, cardiovascular conditions, and even cancer, positioning them as potential therapeutic agents or targets for intervention. In recent years, research into neuropeptides has accelerated, with vast amounts of data continuously accumulating in multiple databases. However, the study of neuropeptides is often impeded by the need for extensive and time-consuming experimental investigations. As a result, computational tools have become essential for the rapid, large-scale identification of neuropeptides. This review systematically discusses neuropeptide-related databases and computational tools. These databases organize extensive data on neuropeptide sequences, structures, and functions. Among these, NeuroPep2.0, with 11,417 neuropeptide entries, is currently the most widely used dataset for neuropeptide prediction. Additionally, this review explores the application of computational approaches in neuropeptide prediction. While early methods predominantly relied on homologous sequence alignment and biochemical feature statistics, recent advances in machine learning have significantly enhanced prediction accuracy and efficiency. Tools such as NeuroPred-PLM and DeepNeuropePred, developed by our research group using protein language models, have substantially improved prediction performance. In conclusion, this review provides a comprehensive overview of current neuropeptide databases and computational tools, offering researchers a thorough survey of available resources and analytical methods, and emphasizing the necessity of continuous optimization to advance neuropeptide research and its therapeutic applications.

Aims: Development of robust and effective methods for uncovering herb interactions and constructing herb–disease associations requires the integration of diverse biological and medical information. A key challenge in Traditional Chinese Medicine (TCM) research is to robustly uncover herb interactions and construct reliable herb–disease associations. This task requires handling the inherently high-dimensional, multi-label, and cross-domain nature of prescription data. Existing approaches provide limited representation capacity and insufficient integration of biomedical knowledge, restricting their ability to capture the complex semantics underlying the relationships between herbs and diseases.

Methods: To address the limitations of existing approaches, we propose MediHerb, a multi-modal enhanced framework for disease inference via herbal knowledge. MediHerb unifies five complementary modalities: molecular sequences, fingerprints, physicochemical properties, graphical prescription representations, and the description of TCM prescriptions into a shared latent space. An attention-based fusion mechanism aligns the semantics across molecular, herbal, and diagnostic levels, enabling multi-granularity reasoning. To further promote accessibility, a lightweight graphical interface has been developed to support interaction with both the model and open datasets.

Results: Experimental results on benchmark datasets demonstrate that MediHerb substantially outperforms existing baselines in herb–disease inference. Beyond predictive accuracy, the learned embeddings and model attention patterns reveal meaningful biological and pharmacological insights, confirming that MediHerb captures the mechanistic underpinnings of herb–disease associations.

Conclusion: MediHerb highlights the potential of knowledge-enhanced multi-modal fusion to bridge molecular, herbal, and clinical semantics, offering a more interpretable and holistic approach to understanding TCM prescriptions.

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.

Aims: Neural networks capable of capturing temporal dependencies in electroencephalogram (EEG) signals hold considerable potential for seizure prediction by modeling the progressive evolution of preictal EEG changes. However, redundant or less informative temporal features may obscure critical preictal patterns, limiting seizure prediction performance. To address this, we developed a neural architecture that effectively leverages informative temporal features to enhance seizure prediction capability.

Methods: We designed a bidirectional long short-term memory (BiLSTM) network enhanced with a channel attention mechanism, termed Attention-BiLSTM, which adaptively emphasizes informative temporal features while reducing information redundancy. We further analyze the model’s attention weights and feature distributions to provide interpretable insights into its decision-making process.

Results: Evaluation on the CHB-MIT scalp EEG dataset demonstrates that Attention-BiLSTM achieves significant performance improvements over the baseline BiLSTM, with an average accuracy of 94.77%, sensitivity of 94.58%, specificity of 94.97%, and an area under the curve of 98.38%. Furthermore, visualization results indicate that the proposed model progressively enhances feature discriminability and directs attention to the most relevant temporal features for seizure prediction.

Conclusion: The proposed Attention-BiLSTM achieves improved performance and interpretability, offering valuable insights to support future development of scalable and generalizable seizure prediction systems.

Neuropeptides are crucial signaling molecules that regulate diverse physiological processes spanning growth, social behavior, learning, memory, metabolism, homeostasis, reproduction, and neural differentiation across both nervous and peripheral systems. Dysregulation of neuropeptides signaling is closely linked to various pathological conditions, such as neurological disorders, metabolic diseases, cardiovascular conditions, and even cancer, positioning them as potential therapeutic agents or targets for intervention. In recent years, research into neuropeptides has accelerated, with vast amounts of data continuously accumulating in multiple databases. However, the study of neuropeptides is often impeded by the need for extensive and time-consuming experimental investigations. As a result, computational tools have become essential for the rapid, large-scale identification of neuropeptides. This review systematically discusses neuropeptide-related databases and computational tools. These databases organize extensive data on neuropeptide sequences, structures, and functions. Among these, NeuroPep2.0, with 11,417 neuropeptide entries, is currently the most widely used dataset for neuropeptide prediction. Additionally, this review explores the application of computational approaches in neuropeptide prediction. While early methods predominantly relied on homologous sequence alignment and biochemical feature statistics, recent advances in machine learning have significantly enhanced prediction accuracy and efficiency. Tools such as NeuroPred-PLM and DeepNeuropePred, developed by our research group using protein language models, have substantially improved prediction performance. In conclusion, this review provides a comprehensive overview of current neuropeptide databases and computational tools, offering researchers a thorough survey of available resources and analytical methods, and emphasizing the necessity of continuous optimization to advance neuropeptide research and its therapeutic applications.

Aims: Development of robust and effective methods for uncovering herb interactions and constructing herb–disease associations requires the integration of diverse biological and medical information. A key challenge in Traditional Chinese Medicine (TCM) research is to robustly uncover herb interactions and construct reliable herb–disease associations. This task requires handling the inherently high-dimensional, multi-label, and cross-domain nature of prescription data. Existing approaches provide limited representation capacity and insufficient integration of biomedical knowledge, restricting their ability to capture the complex semantics underlying the relationships between herbs and diseases.

Methods: To address the limitations of existing approaches, we propose MediHerb, a multi-modal enhanced framework for disease inference via herbal knowledge. MediHerb unifies five complementary modalities: molecular sequences, fingerprints, physicochemical properties, graphical prescription representations, and the description of TCM prescriptions into a shared latent space. An attention-based fusion mechanism aligns the semantics across molecular, herbal, and diagnostic levels, enabling multi-granularity reasoning. To further promote accessibility, a lightweight graphical interface has been developed to support interaction with both the model and open datasets.

Results: Experimental results on benchmark datasets demonstrate that MediHerb substantially outperforms existing baselines in herb–disease inference. Beyond predictive accuracy, the learned embeddings and model attention patterns reveal meaningful biological and pharmacological insights, confirming that MediHerb captures the mechanistic underpinnings of herb–disease associations.

Conclusion: MediHerb highlights the potential of knowledge-enhanced multi-modal fusion to bridge molecular, herbal, and clinical semantics, offering a more interpretable and holistic approach to understanding TCM prescriptions.

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.

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: Neural networks capable of capturing temporal dependencies in electroencephalogram (EEG) signals hold considerable potential for seizure prediction by modeling the progressive evolution of preictal EEG changes. However, redundant or less informative temporal features may obscure critical preictal patterns, limiting seizure prediction performance. To address this, we developed a neural architecture that effectively leverages informative temporal features to enhance seizure prediction capability.

Methods: We designed a bidirectional long short-term memory (BiLSTM) network enhanced with a channel attention mechanism, termed Attention-BiLSTM, which adaptively emphasizes informative temporal features while reducing information redundancy. We further analyze the model’s attention weights and feature distributions to provide interpretable insights into its decision-making process.

Results: Evaluation on the CHB-MIT scalp EEG dataset demonstrates that Attention-BiLSTM achieves significant performance improvements over the baseline BiLSTM, with an average accuracy of 94.77%, sensitivity of 94.58%, specificity of 94.97%, and an area under the curve of 98.38%. Furthermore, visualization results indicate that the proposed model progressively enhances feature discriminability and directs attention to the most relevant temporal features for seizure prediction.

Conclusion: The proposed Attention-BiLSTM achieves improved performance and interpretability, offering valuable insights to support future development of scalable and generalizable seizure prediction systems.