In recent years, the "drug-target-disease" association has become increasingly complex and data-scarce. Existing methods are limited by information loss caused by explicit graph construction when modeling triplet relationships, making it difficult to effectively capture geometric structures and long-range dependencies, thereby affecting prediction performance. To address this, this paper proposes a new method based on point cloud modeling-TriCloud. This method represents each triad as a spatial point cloud, encoding semantic and topological relationships through geometric coordinates, and performs feature learning directly on an unordered point set, avoiding reliance on predefined graph structures. Based on the PointNet architecture, it introduces a multi-view feature extraction and fusion mechanism to enhance the modeling capability of global structures and complex spatial patterns. Experimental results show that TriCloud significantly outperforms existing methods on multiple benchmark datasets, achieving an AUC of 0.9995 and an AUPR of 0.9996, with all metrics ranking first. External validation demonstrates its excellent generalization ability. Feature analysis reveals that the geometric-semantic joint features of positive samples play a dominant role in classification. This study provides an efficient and reliable computational framework for drug repurposing, contributing to the development of precision medicine.

Drug repurposing relies critically on the accurate prediction of drug-target interactions (DTIs). Conventional graph neural network approaches typically model drugs and proteins as isolated nodes, focusing solely on intrinsic attributes such as molecular structure or sequence information. As a result, they often fail to capture complex synergistic effects-such as multi-target regulation. To overcome this limitation, this paper proposes the Topology-Aggregation Enhanced Graph Interaction Network (TAGIN-DTI), a novel framework that shifts the prediction paradigm from a conventional node-level view to a subnetwork-level perspective. The model aggregates topological association features from drug-drug and protein-protein interaction networks via a dual-path Transformer encoder, integrates multi-scale global and local information through a gated fusion mechanism, and incorporates MinHash-based subgraph structure encoding to enhance neighborhood topological representation. Experimental results demonstrate that TAGIN-DTI outperforms existing methods in both prediction accuracy and generalization capability, offering valuable insights for drug repurposing and target discovery.

Drug repositioning is a strategy to identify new therapeutic uses for existing drugs, significantly reducing development costs and time. Although deep learning methods for drug-target interaction prediction have advanced, most models are limited to binary relationships and struggle to capture complex ternary associations among drugs, targets, and diseases. Additionally, limitations in graph structure modeling and node feature representation often constrain their generalization capability. To address these challenges, this paper proposes GraphTransHGN, a framework integrating graph embedding, Transformer feature extraction, and heterogeneous graph neural networks. First, Node2Vec constructs graph representations of drugs and targets, with the Transformer extracting high-level semantic features. Drug-target pairs are then combined into composite nodes (DT_node), and target-disease data are incorporated to form a heterogeneous graph. Finally, the HGTConv models multi-type relationships between DT_node and diseases, enabling end-to-end prediction of potential therapeutic associations. Experimental results demonstrate that GraphTransHGN outperforms mainstream methods across key metrics, achieving an AUC of 0.9916, Recall of 0.9959, and AUPR of 0.9849, which confirms its discriminative power and robustness. The model not only improves prediction accuracy for drug repositioning but also offers a novel technical and theoretical foundation for mechanism-based drug discovery.

BACKGROUND: Drug-drug interactions (DDIs) frequently occur in combination therapy and may cause adverse effects or reduced efficacy. Existing computational approaches often fail to capture both the semantic information in drug sequences and the structural properties of drug molecules, limiting predictive power. RESULTS: We propose MDG-DDI, a deep learning framework that integrates a Frequent Consecutive Subsequence (FCS)-based Transformer encoder with a Deep Graph Network (DGN) to extract complementary semantic and structural features. These representations are fused and fed into a Graph Convolutional Network (GCN) for DDI prediction. Experiments on three benchmark datasets under transductive and inductive settings show that MDG-DDI consistently outperforms state-of-the-art methods, with particularly strong gains when predicting interactions involving unseen drugs. CONCLUSION: By jointly modeling substructure-level semantics and molecular graph structure, MDG-DDI achieves robust and accurate DDI prediction. The framework demonstrates improved generalization and offers potential for enhancing drug safety assessment and discovery.

Drug repositioning is a vital area of biomedicine, where confirming interactions between drugs and specific targets is essential for establishing the efficacy of pharmaceutical agents. Traditional in vitro screening methods have limitations, prompting the use of computer simulations as an effective alternative for predicting drug-target interactions (DTI). This approach has gained significant attention in the scientific community. In this study, we introduce MFCM-DTI, a DTI prediction model that employs multimodal features to accurately capture the intricate interactions between drug molecular structures and key amino acids of target proteins. Our results demonstrate that MFCM-DTI outperforms existing models in prediction accuracy and robustness. Furthermore, MFCM-DTI has been successfully used to predict interactions between key SARS-CoV-2 proteins and existing drugs, providing a solid foundation for developing therapeutic agents against SARS-CoV-2 infection. This study underscores the broad applicability and strong predictive capabilities of MFCM-DTI in drug-protein interaction prediction, opening new avenues for research. The predicted drug targets and interaction data offer valuable insights for future experimental validation and clinical trials, potentially driving innovation in the biomedical field.

Bioinformatics is a rapidly growing field that involves the application of computational methods to analyze and interpret biological data. One important task in bioinformatics is predicting the drug-target affinity (DTA), which plays a significant role in drug discovery through virtual screening. Effectively predicting the association between drug molecules and target molecules can speed up the drug discovery process. The DTA can be quantitatively measured. This quantifiable affinity is more precise than a simple binary relationship. In this study, we propose a deep learning model for DTA prediction that utilizes the encoder module of the Transformer architecture. Our proposed model utilizes Convolutional Neural Networks (CNNs) and the encoder module of Transformer to characterize protein and drug sequences. The model outperforms some methods such as KronRLS, SimBoost and DeepDTA as evidenced by superior evaluation metrics such as Mean Squared Error (MSE), Concordance Index (CI), and Regression toward the Mean Index $\left( {r_m^2} \right)$. These results demonstrate the effectiveness of the Transformer’s encoder in extracting meaningful representations from sequences, thereby improving the accuracy of DTA prediction. Deep learning models for DTA prediction can accelerate drug discovery by identifying drug candidates with high binding affinity to specific targets. Compared to traditional methods, the use of machine learning technology enables more effective and efficient drug discovery.

The interactions of microRNAs and lncRNAs play important roles in biological activities. The microRNA-lncRNA prediction which has been a research focus in the bioinformatics faces many challenges. In the study, a new framework called Pmli-TF is proposed to predict plant miRNA-lncRNA interactions. Pmli-TF provides a four-step process: Input Embedding, Positional Encoding, Multi-Head Attention and Max Pooling. Our algorithm is compared with the existing models on 2 benchmark datasets. The results show that Pmli-TF performs better than other methods.

Accumulating evidence has shown that microRNAs (miRNAs) play crucial roles in different biological processes, and their mutations and dysregulations have been proved to contribute to tumorigenesis. In silico identification of disease-associated miRNAs is a cost-effective strategy to discover those most promising biomarkers for disease diagnosis and treatment. The increasing available omics data sources provide unprecedented opportunities to decipher the underlying relationships between miRNAs and diseases by computational models. However, most existing methods are biased towards a single representation of miRNAs or diseases and are also not capable of discovering unobserved associations for new miRNAs or diseases without association information. In this study, we present a novel computational method with adaptive multi-source multi-view latent feature learning (M2LFL) to infer potential disease-associated miRNAs. First, we adopt multiple data sources to obtain similarity profiles and capture different latent features according to the geometric characteristic of miRNA and disease spaces. Then, the multi-modal latent features are projected to a common subspace to discover unobserved miRNA-disease associations in both miRNA and disease views, and an adaptive joint graph regularization term is developed to preserve the intrinsic manifold structures of multiple similarity profiles. Meanwhile, the Lp,q-norms are imposed into the projection matrices to ensure the sparsity and improve interpretability. The experimental results confirm the superior performance of our proposed method in screening reliable candidate disease miRNAs, which suggests that M2LFL could be an efficient tool to discover diagnostic biomarkers for guiding laborious clinical trials.

Computational methods accurately prioritizing latent disorder genes require for all kinds of biological information. But for the defection of a single type of biological data has a negative impact on the identification of genes causing diseases. To address the limitation, computing approaches often integrate different type of biological data. In the study, a novel algorithm PDGPC (Predicting Disease Genes with Protein Complexes) is proposed. It utilizes protein subcellular localizations to improve the reliability of the protein-protein interactions and constructs the weighted networks. And then, PDGPC builds the disease-specific networks by utilizing the protein complexes which are detected from the weighted networks through the non-negative matrix factorization. Finally, PDGPC scores all proteins in the disease-specific networks in terms of WDC. The literature retrieving method tests the correlations of top genes with more higher scores with diseases. Results show PDGPC discover some novel candidate disease genes which are valuable references for the biomedical scientists.

BACKGROUND: In biomedical information extraction, event extraction plays a crucial role. Biological events are used to describe the dynamic effects or relationships between biological entities such as proteins and genes. Event extraction is generally divided into trigger detection and argument recognition. The performance of trigger detection directly affects the results of the event extraction. In general, the traditional method is used to address the trigger detection as a classification task, as well as the use of machine learning or rules method, which construct many features to improve the classification results. Moreover, the classification model only recognizes triggers composed of single words, whereas for multiple words, the result is unsatisfactory. RESULTS: The corpus of our model is MLEE. If we were to only use the biomedical LSTM and CRF model without other features, the F-score would reach about 78.08%. Comparing entity to part of speech (POS), we find the entity features more conducive to the improvement of performance of detection, with the F-score potentially reaching about 80%. Furthermore, we also experiment on the other three corpora (BioNLP 2009, BioNLP 2011, and BioNLP 2013) to verify the generalization of our model. Hence, F-scores can reach more than 60%, which are better than the comparative experiments. CONCLUSIONS: The trigger recognition method based on the sequence annotation model does not require initial complex feature engineering, and only requires a simple labeling mechanism to complete the training. Therefore, generalization of our model is better compared to other traditional models. Secondly, this method can identify multi-word triggers, thereby improving the F-scores of trigger recognition. Thirdly, details on the entity have a crucial impact on trigger detection. Finally, the combination of character-level word embedding and word-level word embedding provides increasingly effective information for the model; therefore, it is a key to the success of the experiment.

BACKGROUND: Essential proteins are indispensable to the survival and development process of living organisms. To understand the functional mechanisms of essential proteins, which can be applied to the analysis of disease and design of drugs, it is important to identify essential proteins from a set of proteins first. As traditional experimental methods designed to test out essential proteins are usually expensive and laborious, computational methods, which utilize biological and topological features of proteins, have attracted more attention in recent years. Protein-protein interaction networks, together with other biological data, have been explored to improve the performance of essential protein prediction. RESULTS: The proposed method SCP is evaluated on Saccharomyces cerevisiae datasets and compared with five other methods. The results show that our method SCP outperforms the other five methods in terms of accuracy of essential protein prediction. CONCLUSIONS: In this paper, we propose a novel algorithm named SCP, which combines the ranking by a modified PageRank algorithm based on subcellular compartments information, with the ranking by Pearson correlation coefficient (PCC) calculated from gene expression data. Experiments show that subcellular localization information is promising in boosting essential protein prediction.

Essential proteins play a crucial role in the survival and development process of life, as they provide all available nutrients to maintain life. Therefore, many researchers pay attention to the identification of essential proteins. As experiments methods are usually costly and time-consuming, more and more computational algorithms have been developed to discover essential proteins based on biological and topological features. Given that the subcellular localization is very important in understanding protein-protein interaction, in this paper, a novel method is proposed to predict essential proteins, which integrates the subcellular compartments information with Pearson correlation coefficient (PCC) of gene expression data. We name this method SCP in this paper. In order to evaluate the prediction performance of our method, several experiments are carried out to compare SCP with other methods. The results demonstrate that SCP has a better prediction performance of essential proteins than other methods.

Disease gene identification is a key step to understand the cellular mechanisms associated with a specific disease. Compared with biological experiments, computational predictions of disease genes are cheaper and more effortless. Many computational methods are used to detect causal genes for diseases on the protein-protein interaction (PPI) networks generated by the high-throughput technology. However, the accuracy of these methods need to be improved due to the false interactions in the PPI data. To deal with the challenge, other methods are proposed via the integration of biological information from different sources with the PPI networks. In this work, a new algorithm AIDG is developed to predict disease genes. First, the weighted PPI networks are built by incorporating the protein subcellular localization information into the human PPI networks. Next, all of disease candidate genes are scored in terms of a iteration function. Finally, they are ranked on descending order of their scores. The top candidates are considered as potential disease genes. The results from the leave-one-out crossing validation (LOOCV) show that AIDG outperforms other similar methods like DADA and ToppNet.

Essential proteins are vital for an organism's viability under a variety of conditions. There are many experimental and computational methods developed to identify essential proteins. Computational prediction of essential proteins based on the global protein-protein interaction (PPI) network is severely restricted because of the insufficiency of the PPI data, but fortunately the gene expression profiles help to make up the deficiency. In this work, Pearson correlation coefficient (PCC) is used to bridge the gap between PPI and gene expression data. Based on PCC and edge clustering coefficient (ECC), a new centrality measure, i.e., the weighted degree centrality (WDC), is developed to achieve the reliable prediction of essential proteins. WDC is employed to identify essential proteins in the yeast PPI and e-Coli networks in order to estimate its performance. For comparison, other prediction technologies are also performed to identify essential proteins. Some evaluation methods are used to analyze the results from various prediction approaches. The prediction results and comparative analyses are shown in the paper. Furthermore, the parameter λ in the method WDC will be analyzed in detail and an optimal λ value will be found. Based on the optimal λ value, the differentiation of WDC and another prediction method PeC is discussed. The analyses prove that WDC outperforms other methods including DC, BC, CC, SC, EC, IC, NC, and PeC. At the same time, the analyses also mean that it is an effective way to predict essential proteins by means of integrating different data sources.

The vast amount of genes and proteins that participate in biological networks imposes the need for determination of protein complexes within the network in order to reduce the complexity, while these complexes will be the first step in deciphering the composite genetic or cellular interactions of the overall network.

Essential proteins are vital for an organism's viability under a variety of conditions. Computational prediction of essential proteins based on the global protein-protein interaction (PPI) network is severely restricted because of the insufficiency of the PPI data, but fortunately the gene expression profiles help to make up the deficiency. In this work, Pearson correlation coefficient (PCC) is used to bridge the gap between PPI and gene expression data. Based on PCC and Edge Clustering Coefficient (ECC), a new centrality measure, i.e., the weighted degree centrality (WDC), is developed to achieve the reliable prediction of essential proteins. WDC is employed to identify essential proteins in the yeast PPI network in order to estimate its performance. For comparison, other prediction technologies are also performed to identify essential proteins. Some evaluation methods are used to analyze the results from various prediction approaches. The analyses prove that WDC outperforms other state-of-the-art ones. At the same time, the analyses also mean that it is an effective way to predict essential proteins by means of integrating different data sources.

BACKGROUND: Cellular systems are highly dynamic and responsive to cues from the environment. Cellular function and response patterns to external stimuli are regulated by biological networks. A protein-protein interaction (PPI) network with static connectivity is dynamic in the sense that the nodes implement so-called functional activities that evolve in time. The shift from static to dynamic network analysis is essential for further understanding of molecular systems. RESULTS: In this paper, Time Course Protein Interaction Networks (TC-PINs) are reconstructed by incorporating time series gene expression into PPI networks. Then, a clustering algorithm is used to create functional modules from three kinds of networks: the TC-PINs, a static PPI network and a pseudorandom network. For the functional modules from the TC-PINs, repetitive modules and modules contained within bigger modules are removed. Finally, matching and GO enrichment analyses are performed to compare the functional modules detected from those networks. CONCLUSIONS: The comparative analyses show that the functional modules from the TC-PINs have much more significant biological meaning than those from static PPI networks. Moreover, it implies that many studies on static PPI networks can be done on the TC-PINs and accordingly, the experimental results are much more satisfactory. The 36 PPI networks corresponding to 36 time points, identified as part of this study, and other materials are available at http://bioinfo.csu.edu.cn/txw/TC-PINs.