Drug combination therapy demonstrates more significant efficacy than monotherapy in cancer treatment. Despite the proposal of several computational approaches aimed at effectively identifying synergistic drug combinations, challenges persist due to inadequate multi-level learning within multimodal data. Furthermore, existing models still struggle to adequately capture the complex biological network interactions between drug combinations and cell lines. To overcome these issues, we propose a novel hypergraph neural network method for synergistic drug combination prediction. This method integrates multi-view feature learning and enhanced hypergraph neural networks to improve drug combination prediction. First, multi-view learning is independently applied to the multimodal data of drugs and cell lines. This framework employs a fine-tuned ChemBERTa model enhanced by contrastive learning to effectively capture the contextual information of drug SMILES. Second, enhanced hypergraph neural networks equipped with a multi-head attention mechanism are designed to capture the complex topological information between drugs and cell lines and to address the limited ability of the hypergraph to capture global information. Third, the similarity-based multi-task supervision module further stabilizes the model. The experimental results show that our method outperforms state-of-the-art methods in various scenarios, including leave-drug-combination-out, leave-cell-out, and leave-drug-out scenarios. Specifically, in the leave-drug combination-out scenario, our method achieves a Mean Squared Error of 163.635, a Root Mean Squared Error of 12.792, and a Pearson Correlation Coefficient of 0.751. Finally, a case study demonstrates the efficacy of the model in predicting novel synergistic drug combinations.

Sleep plays a vital role in human life, and its quality has a direct impact on overall health. Sleep staging is a crucial process and a key indicator used to evaluate sleep quality. This paper proposes a sleep staging method based on statistical mode of multi-threshold neighborhood extreme (SMNE). We employ the discrete wavelet transform (DWT) in conjunction with a data enhancement algorithm to preproces the EEG signals, improving their quality through a combined approach based on signal-to-noise ratio(SNR) assessment and signal overlap analysis. Then, the extremes of EEG signal are classified into 5 distinct states. Multi-threshold is applied to differences in 5-state extreme value matrix to define and extract patterns. These patterns are then statistically encoded. The extracted codes, representing the various patterns, are input into 5 weighting layers. Then, the extracted codes are fed into grey wolf optimization (GWO) to denote threshold for SNR and SMNE feature. Finally, the features derived from this process are input into a Random Forest (RF) classifier. The SleepEDFx dataset shows accuracy of 94.6%, kappa coefficient of 0.92 and F1-score of 89.3%. For the SleepEDF-20 dataset, the corresponding metrics are accuracy of 96.3%, kappa of 0.94 and F1-score of 94.2%. Meanwhile, the ISRUC-Sleep dataset achieves accuracy of 88.5%, kappa value of 0.83 and F1-score of 86.5%.

Spatially resolved transcriptomics (SRT) technologies facilitate the exploration of cell fates or states within tissue microenvironments. Despite these advances, the field has not adequately addressed the regulatory heterogeneity influenced by microenvironmental factors. Here, we propose a novel Spatially Aligned Graph Transfer Learning (SpaGTL), pretrained on a large-scale multi-modal SRT data of about 100 million cells/spots to enable inference of context-specific spatial gene regulatory networks across multiple scales in data-limited settings. As a novel cross-dimensional transfer learning architecture, SpaGTL aligns spatial graph representations across gene-level graph transformers and cell/spot-level manifold-dominated variational autoencoder. This alignment facilitates the exploration of microenvironmental variations in cell types and functional domains from a molecular regulatory perspective, all within a self-supervised framework. We verified SpaGTL's precision, robustness, and speed over existing state-of-the-art algorithms and show SpaGTL's potential that facilitates the discovery of novel regulatory programs that exhibit strong associations with tissue functional regions and cell types. Importantly, SpaGTL could be extended to process multi-slice SRT data and map molecular regulatory landscape associated with three-dimensional spatial-temporal changes during development.

Drug-target interaction (DTI) prediction is crucial for drug development and repositioning. Methods using heterogeneous graph neural networks (HGNNs) for DTI prediction have become a promising approach, with attention-based models often achieving excellent performance. However, these methods typically overlook edge features when dealing with heterogeneous biomedical networks. We propose a heterogeneous network-based contrastive learning method called HNCL-DTI, which designs a heterogeneous graph attention network to predict potential/novel DTIs. Specifically, our HNCL-DTI utilizes contrastive learning to collaboratively learn node representations from the perspective of both node-based and edge-based attention within the heterogeneous structure of biomedical networks. Experimental results show that HNCL-DTI outperforms existing advanced baseline methods on benchmark datasets, demonstrating strong predictive ability and practical effectiveness. The data and source code are available at https://github.com/Zaiwen/HNCL-DTI.

Single-cell RNA sequencing (scRNA-seq) data analysis is pivotal for understanding cellular heterogeneity. However, the high sparsity and complex noise patterns inherent in scRNA-seq data present significant challenges for traditional clustering methods. To address these issues, we propose a deep clustering method, Attention-Enhanced Structural Deep Embedding Graph Clustering (scASDC), which integrates multiple advanced modules to improve clustering accuracy and robustness. Our approach employs a multi-layer graph convolutional network (GCN) to capture high-order structural relationships between cells, termed as the graph autoencoder module. We introduce a ZINB-based autoencoder module that extracts content information from the data and learns latent representations of gene expression. These modules are further integrated through an attention fusion mechanism, ensuring effective combination of gene expression and structural information at each layer of the GCN. Additionally, a self-supervised learning module is incorporated to enhance the robustness of the learned embeddings. Extensive experiments demonstrate that scASDC outperforms existing state-of-the-art methods, providing a robust and effective solution for single-cell clustering tasks. All code and public datasets used in this paper are available at https://github.com/wenwenmin/scASDC.

Enzymatic Reaction feasibility prediction is used to determine whether the reaction generated by computational methods can actually occur, which can effectively reduce the complexity of synthetic pathway design. Existing methods often use SMILES or molecular fingerprints to represent molecules, resulting in a lack of molecular structural information. Although some GNNs have been leveraged to solve this problem, the complexity of the intra and inter-substructures interactions of molecules in microorganisms makes it difficult for traditional GNNs to accurately model it. To address these problems, we propose a subgraph-based self-supervised learning framework to predict the feasibility of enzymatic reactions. Specifically, we first propose a subgraph-based two-branch graph neural network. This network leverages the atom graph and substructure graph of a molecule to thoroughly capture its structural and semantic information. Besides, a subgraph interaction module is designed to facilitate the full integration of features. Subsequently, we propose a domain knowledge-guided self-supervised learning task, utilizing molecular fingerprints and substructures to capture the consistency between them effectively. The experimental results show that the proposed method outperforms existing state-ofthe-art methods significantly on all datasets.

In order to address the issue that previous deep learning methods for classifying pathological images cannot adaptively learn features, we propose an end-to-end attention pooling method based on a multi-instance learning patch scoring model. Our method integrates feature extraction and classification into a unified framework that is conducive to extracting the most valuable features. In this model, a patch scoring method is constructed by a multi-instance learning method firstly and then the partial patches selected by the patches scoring model are classified using an end-to-end classification model that incorporates an attention pooled mechanism. To make the pathological image classification mechanism more compatible with the pathologist diagnosis method, we use the squared average normalization function instead of the softmax function to optimize the feature extraction and fusion process, so that the high score patches in positive pathological images receive more attention weights, thus giving better interpretability to the classification results. Experiments on publicly available datasets TCGA_BRCA show a significant improvement in the performance of our approach over other work.

BACKGROUND: Increasing biomedical studies have shown that the dysfunction of miRNAs is closely related with many human diseases. Identifying disease-associated miRNAs would contribute to the understanding of pathological mechanisms of diseases. Supervised learning-based computational methods have continuously been developed for miRNA-disease association predictions. Negative samples of experimentally-validated uncorrelated miRNA-disease pairs are required for these approaches, while they are not available due to lack of biomedical research interest. Existing methods mainly choose negative samples from the unlabelled ones randomly. Therefore, the selection of more reliable negative samples is of great importance for these methods to achieve satisfactory prediction results. RESULTS: In this study, we propose a computational method termed as KR-NSSM which integrates two semi-supervised algorithms to select more reliable negative samples for miRNA-disease association predictions. Our method uses a refined K-means algorithm for preliminary screening of likely negative and positive miRNA-disease samples. A Rocchio classification-based method is applied for further screening to receive more reliable negative and positive samples. We implement ablation tests in KR-NSSM and find that the combination of the two selection procedures would obtain more reliable negative samples for miRNA-disease association predictions. Comprehensive experiments based on fivefold cross-validations demonstrate improvements in prediction accuracy on six classic classifiers and five known miRNA-disease association prediction models when using negative samples chose by our method than by previous negative sample selection strategies. Moreover, 469 out of 1123 selected positive miRNA-disease associations by our method are confirmed by existing databases. CONCLUSIONS: Our experiments show that KR-NSSM can screen out more reliable negative samples from the unlabelled ones, which greatly improves the performance of supervised machine learning methods in miRNA-disease association predictions. We expect that KR-NSSM would be a useful tool in negative sample selection in biomedical research.

To assist drug development, many computational methods have been proposed to identify potential drug-disease treatment associations before wet experiments. Based on the assumption that similar drugs may treat similar diseases, most methods need the similarities of drugs and diseases, and they will not work if the biological or chemical features for computing similarities are missing. Besides, being lack of validated negative samples in the drug-disease associations data, most methods simply select some unlabeled samples as negative ones, which may introduce noises. Herein, we propose a new method (TS-SVD) which only uses those known drug-protein, disease-protein and drug-disease interactions to predict the potential drug-disease associations. In a constructed drug-protein-disease heterogeneous network, we consider the common neighbors of drugs and diseases to obtain the topological similarity. Then the topological similarity matrix of drugs (diseases) will be used to get the low dimensional embedding representations of drug-disease pairs. Finally, a Random Forest classifier is trained to do the prediction. To train a more reasonable model, we select out some reliable negative samples based on the k-step neighbors relationships between drugs and diseases. Compared with some state-of-the-art methods, we use less information but achieve better or comparable performance. Meanwhile, our strategy for selecting reliable negative samples can improve the performances of these methods.

BACKGROUND: In the field of drug repositioning, it is assumed that similar drugs may treat similar diseases, therefore many existing computational methods need to compute the similarities of drugs and diseases. However, the calculation of similarity depends on the adopted measure and the available features, which may lead that the similarity scores vary dramatically from one to another, and it will not work when facing the incomplete data. Besides, supervised learning based methods usually need both positive and negative samples to train the prediction models, whereas in drug-disease pairs data there are only some verified interactions (positive samples) and a lot of unlabeled pairs. To train the models, many methods simply treat the unlabeled samples as negative ones, which may introduce artificial noises. Herein, we propose a method to predict drug-disease associations without the need of similarity information, and select more likely negative samples. RESULTS: In the proposed EMP-SVD (Ensemble Meta Paths and Singular Value Decomposition), we introduce five meta paths corresponding to different kinds of interaction data, and for each meta path we generate a commuting matrix. Every matrix is factorized into two low rank matrices by SVD which are used for the latent features of drugs and diseases respectively. The features are combined to represent drug-disease pairs. We build a base classifier via Random Forest for each meta path and five base classifiers are combined as the final ensemble classifier. In order to train out a more reliable prediction model, we select more likely negative ones from unlabeled samples under the assumption that non-associated drug and disease pair have no common interacted proteins. The experiments have shown that the proposed EMP-SVD method outperforms several state-of-the-art approaches. Case studies by literature investigation have found that the proposed EMP-SVD can mine out many drug-disease associations, which implies the practicality of EMP-SVD. CONCLUSIONS: The proposed EMP-SVD can integrate the interaction data among drugs, proteins and diseases, and predict the drug-disease associations without the need of similarity information. At the same time, the strategy of selecting more reliable negative samples will benefit the prediction.

Drug discovery is a cost expensive and time consuming process. Approved drugs have favorable or validated pharmacokinetic properties and toxicological profiles. Therefore repurposing approved drugs to new diseases can potentially avoid expensive costs associated with the early-stage testing. In this work, we propose to integrate drug-drug, disease-disease, gene-gene and drug-disease associations to reposition the approved drugs. Firstly, multiple sources of data are integrated into three layers: the chemical/phenotype layer, the gene/machanism layer and the treatment layer. Secondly, the drug-drug and disease-disease similarities in three layers are respectively computed and then combined together. Finally, based on the hypothesis that similar drugs may treat similar diseases, we model the drug repositioning problem as an optimal problem and propose a semi-supervised graph cut (SSGC) algorithm to solve the problem. The experimental results show that integrating multiple sources of data can achieve better performances than only considering single kind of data, and our method outperforms three representative approaches. Moreover, the predicted top-ranked repositioned relations have been reported in literature, illustrating the usefulness of our method in practice. The predicted results are available at https://github.com/wgs666/SSGC.git.