The prediction of drug action mechanisms is a core challenge in precision drug design. Existing research mainly focuses on binary classification tasks predicting the existence of drug-target interactions and regression tasks pre dicting affinity, while significant gaps remain in computational methods for revealing drug action mechanisms. Traditional methods rely on target 3D structures or manually designed feature encoding, while current compu tational methods overlook the synergistic modeling of molecular properties and network topology. To address this, SGCA-DTI is proposed in this article, which integrates attribute features and signed network topology fea tures for activation/inhibition effect prediction. At the attribute feature level, it integrates drug dual-fingerprint encoding with structure-aware protein embeddings from ProtT5, leveraging its self-attention mechanism to cap ture evolutionary and physicochemical patterns. At the network modeling level, a bipartite network containing activation/inhibition signs is constructed, and signed bipartite graph contrastive learning is used to model the relationships and capture network topology features. Experimental results on benchmark datasets show that SGCA-DTI performs excellently, achieving an AUC of 0.9584 and an AUPR of 0.9279. Case studies on Midazolam for anxiolysis and Agomelatine and Maprotiline for antidepressant therapy show that SGCA-DTI accurately predicts drug-target activation/inhibition effects, demonstrating its capability in practical applications.

MicroRNAs (miRNAs) play a vital role in regulating a wide range of biological functions and are key players in the development of many complex human diseases, making them novel therapeutic targets for drug development. Given the high expenses and time demands of traditional experimental methods, it is essential to develop efficient computational approaches for predicting miRNA-drug interactions (MDIs). This article presents a dual-channel learning framework, SSMDI, based on structural features and Signed Bipartite Graph Neural Network (SBGNN) for predicting MDIs. Firstly, Graph Isomorphism Networks (GIN) is employed to extract molecular graph features of drugs. Meanwhile, a combined framework of Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM) network and Self-attention Mechanism is utilized to capture sequence features of miRNAs. Compared with traditional networks, signed networks can deliver richer semantic information in drugs and miRNAs. Therefore, SBGNN is then used to aggregate and update the signed topological features of miRNAs and drugs. Finally, structural and signed topological features are integrated to predict MDIs. The predictive performance of the model is evaluated using 5-fold cross-validation (CV), achieving AUC of 0.9447 and AUPR of 0.9238. The case study further demonstrates the effectiveness of SSMDI in predicting MDIs. In summary, the SSMDI model proves to be an accurate tool for predicting MDIs, which holds significant implications for drug development and miRNA-based therapeutic research.

Drug-Drug Interaction is characterized by a modification in the action of one drug due to its concurrent use with another. It involves the safety and universality of drugs, and is one of the most meaningful issues in clinical drug combination therapy and drug development. We prefer to use computational methods to achieve DDI prediction in order to achieve large-scale prediction. This article designs a DDI prediction model DualC based on the layer attention mechanism of Graph Convolutional Network and 1 Dimensional-Convolutional Neural Network to extract topological and structural information of drugs. First, the DDI network is obtained from the drug relationship data in the database and the drug similarity network is calculated with the help of drug target features, then they are constructed into a heterogeneous network. Next, the layer attention mechanism and Graph Convolutional Network are used to learn the topological information. Subsequently, the structural information is acquired from the chemical substructure similarity matrix utilizing 1 Dimensional-Convolutional Neural Network. Finally, use the sigmoid function for DDI prediction. The experimental results show advantages of DualC which AUC reaches 0.965 and ACC reaches 0.973. The case study further proves DualC has certain practical significance.

Computational approaches for predicting drug-target interactions (DTIs) are pivotal in advancing drug discovery. Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information. To comprehensively consider network information, we propose DHGT-DTI, a novel deep learning-based approach for DTI prediction. Specifically, we capture the local and global structural information of the network from both neighborhood and meta-path perspectives. In the neighborhood perspective, we employ a heterogeneous graph neural network (HGNN), which extends Graph Sample and Aggregate (GraphSAGE) to handle diverse node and edge types, effectively learning local network structures. In the meta-path perspective, we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths, such as "drug-disease-drug", and use an attention mechanism to fuse information across multiple meta-paths. The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method. Furthermore, DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy. Comprehensive experiments on two benchmark datasets validate the superiority of DHGT-DTI over existing baseline methods. Additionally, case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases.

Polypharmacy is a common means of clinical treatments, but detecting drug-drug interactions (DDIs) behind unexpected effects can be costly and faces clinical limitations. Recently, graph neural networks (GNNs) have demonstrated encouraging performance in predicting DDIs. However, most studies overlook the comprehensive aspects of DDIs, such as the coexistence of types of pharmacological changes and the asymmetric roles of drugs. In this article, we define new prediction tasks, taking into account both enhancive or depressive changes and the roles of drugs, and then establish spectral GNNs to predict comprehensive information of DDIs. First, we formally define several tasks, including joint prediction tasks designed to leverage both types and directions. These tasks deduce to sub-tasks in previous studies. Then, we propose a unified framework, the MKMGCN-DDI, via introducing two Magnetic Laplacian matrices to encode comprehension information within DDIs, defining multiple graph filters, and designing multiple-kernel based Magnetic graph convolutional networks (MKMGCN). Experiments across three datasets show that it not only has good adaptability to multiple tasks but also significantly improves results on simple tasks. Case studies on breast neoplasms and lung neoplasms verify its feasibility, as over half of top-10 items are supported.

The prediction of mechanisms within drug-target interactions (DTIs) can boost the drug discovery process, which has traditionally relied on time-consuming and expensive laboratory experiments. Despite much more attention has been paid to predicting DTIs, but few studies focused on their activating/inhibiting mechanisms. In this work, we model DTIs on signed heterogeneous networks, through categorizing activating/inhibiting DTIs into signed links, and accordingly introducing the coherence/incoherence between drugs on a common target to construct signed drug-drug links. We propose a multi-filter based signed heterogeneous graph convolutional network (MFSHGCN) for drugs and targets embedding, via employing dual filters on both the signed drug-drug sub-graph and the signed DTI sub-graph to converge the spectral information from positive and negative edges. We further put forward an end-to-end framework to predict activation and inhibition within DTIs. The comparison results demonstrate the introduction of coherence/incoherence of drug pairs and the design of our multi-filter system can effectively improve the prediction metrics, even without relying on rich node information and interactions from drug pairs or target pairs. Case studies on breast cancer and lung cancer confirm the model's feasibility.

Drug-drug interaction (DDI) events can lead to unintended adverse consequences, and their prediction is an important part of medication safety. In recent years, models based on Graph Neural Networks (GNNs) have made significant progress in this area, but existing methods tend to utilize only drug structure information or interaction information. In addition, due to the scarcity of labeled instances of rare events, predicting these events faces challenges. In this study, we propose a novel graph neural network model based on Multi-relational Contrastive Learning Graph Neural Network (MRCGNN), Multi-Relational Enhanced Graph Neural Network (MRE-GNN), which can incorporate drug structure information and a multi-relational contrastive learning strategy to capture the implicit features of rare DDI events. By deploying GNN on the multi-relational DDI event graph, the model extracts the structural features extracted from the drug molecular graph and further enhances the capture ability by multi-view negative correspondence enhancement strategies (such as random permutation of nodes and edges, introduction of noise, adding or removing nodes or edges, or both). Finally, the model combines the drug structural features with the drug pair representation to predict DDI events. Both on Deng's and Ryu’s datasets, MRE-GNN performs better than the single eigenvector model and shows satisfactory results in the prediction of rare DDI events.

In the treatment of complex diseases, single drug therapy is limited by resistance and tolerance, making the exploration of efficient drug combinations crucial in cancer research. When evaluating drug efficacy, traditional deep learning methods analyze single-drug sensitivity and drug combination synergy in isolation, which cannot capture the complex internal relationship between them, resulting in prediction deviation. To overcome these limitations, this article proposes a novel drug combination synergy prediction model called MTDSN (Multi-Task Deep Synergy Network), which integrates multi-task learning and deep neural networks to simultaneously predict single-drug sensitivity and drug combination synergy. During the model construction, the Autoencoder integrated with Convolutional Block Attention Module (CBAM) is used to reduce the dimension of input features, and then the drug features and cell line features are connected and input into the shared module embedded with cross-stitch mechanism to exchange information. Finally, the features of each task are input into different task-specific branches to obtain the synergy score of the drug combination, the sensitivity score of the single drug and their corresponding classification results. Evaluated on the O'Neil dataset, MTDSN achieves the lowest mean squared error (MSE) and highest Pearson correlation coefficient (PCC) in drug synergy prediction, with an ROC-AUC of 0.92 and accuracy of 0.95 in the classification task, demonstrating substantial improvements in predictive efficacy.

Recently, Nominal Compound Chain Extraction (NCCE) has been proposed to detect related mentions in a document to improve understanding of the document’s topic. NCCE involves longer span detection and more complicated rules for relation decisions, making it more difficult than previous chain extraction tasks, such as coreference resolution. Current methods achieve certain progress on the NCCE task, but they suffer from insufficient syntax information utilization and incomplete mention relation mining, which are helpful for NCCE. To fill these gaps, we propose a syntax-guided model using a triaffine interaction to improve the performance of the NCCE task. Instead of solely relying on the text information to detect compound mentions, we also utilize the noun-phrase (NP) boundary information in constituency trees to incorporate prior boundary knowledge. In addition, we use biaffine and triaffine operations to mine the mention interactions in the local and global context of a document. To show the effectiveness of our methods, we conduct a series of experiments on a human-annotated NCCE dataset. Experimental results show that our model significantly outperforms the baseline systems. Moreover, in-depth analyses reveal the effect of utilizing syntactic information and mention interactions in the local and global contexts.

The prediction of drug-drug interactions (DDIs) and drug-target interactions (DTIs) is currently a prominent area of interest within the domain of drug data analysis. Both types of interactions can be modeled as signed links in a graph, in which the attributes of nodes usually have multiple sources. Graph convolutional network (GCN) models, which have theoretical inspirations from graph signal processing (GSP), are effective deep learning methods for drug research. However, it is still open to explore GSP based GCN models to sufficiently utilize spectral information from both signed graph structures and multi-source node attributes. In this study, we propose a multi-filter based signed graph convolutional network (MFSGCN) to handle multiple features of nodes on signed networks. We first extend a rational filter, which is parameterized and has theoretical power and meanings, to signed graphs. Subsequently, we leverage multiple attributes as multi-channel graph signals and implement MFSGCN via learning different parameters of filters. For the sign prediction problems on homogeneous DDIs networks and heterogeneous DTIs networks, we put forward MFSGCN-DDI and MFSGCN-DTI, respectively. The experimental results verify the validity and generalization of MFSGCN and demonstrate the impact of different features and effectiveness of multiple filters.

"DSP technology and its application"is an important and highly practical professional core course for the electronic information specialty.It is usually equipped with corresponding experimental teaching to assist theoretical teaching,to combine theory with practice and improve the teaching effect.This paper designs a comprehensive experimental teaching case that integrates signal generation,acquisition,and processing based on the DSP chip.The purpose is to enable students to systematically understand the basic working principle of the DSP chip,establish the overall concept of the DSP system,improve hands-on practice,develop independent analysis and problem-solving skills,and enhance independent innovation awareness,to meet the needs of emerging engineering talent training.

Drug repositioning is critical to drug development. Previous drug repositioning methods mainly constructed drug–disease heterogeneous networks to extract drug–disease features. However, these methods faced difficulty when we are using structurally simple models to deal with complex heterogeneous networks. Therefore, in this study, the researchers introduced a drug repositioning method named DRDSA. The method utilizes a deep sparse autoencoder and integrates drug–disease similarities. First, the researchers constructed a drug–disease feature network by incorporating information from drug chemical structure, disease semantic data, and existing known drug–disease associations. Then, we learned the low-dimensional representation of the feature network using a deep sparse autoencoder. Finally, we utilized a deep neural network to make predictions on new drug–disease associations based on the feature representation. The experimental results show that our proposed method has achieved optimal results on all four benchmark datasets, especially on the CTD dataset where AUC and AUPR reached 0.9619 and 0.9676, respectively, outperforming other baseline methods. In the case study, the researchers predicted the top ten antiviral drugs for COVID-19. Remarkably, six out of these predictions were subsequently validated by other literature sources. Schematic diagrams of data processing and DRDSA model. A Construction of drug and disease feature vectors, B The workflow of DRDSA model.

Drug-drug interaction refers to taking the two drugs may produce certain reaction which may be a threat to patients' health, or enhance the efficacy helpful for medical work. Therefore, it is necessary to study and predict it. In fact, traditional experimental methods can be used for drug-drug interaction prediction, but they are time-consuming and costly, so we prefer to use more accurate and convenient calculation methods to predict the unknown drug-drug interaction. In this paper, we proposed a deep learning framework called MSResG that considers multi-sources features of drugs and combines them with Graph Auto-Encoder to predicting. Firstly, the model obtains four feature representations of drugs from the database, namely, chemical substructure, target, pathway and enzyme, and then calculates the Jaccard similarity of the drugs. To balance different drug features, we perform similarity integration by finding the mean value. Then we will be comprehensive similarity network combined with drug interaction network, and encodes and decodes it using the graph auto-encoder based on residual graph convolution network. Encoding is to learn the potential feature vectors of drugs, which contain similar information and interaction information. Decoding is to reconstruct the network to predict unknown drug-drug interaction. The experimental results show that our model has advanced performance and is superior to other existing advanced methods. Case study also shows that MSResG has practical significance.

对鸣笛声的准确识别是机动车鸣笛抓拍系统得以运用的关键.为了克服单一特征对鸣笛声表征不足的缺陷,提高识别的准确性,文章将Mel频率倒谱系数(Mel Frequency Cepstrum Coefficient,MFCC)与Gama频率倒谱系数(Gammatone Frequency Cepstrum Coefficient,GFCC)融合得到M-GFCC特征,并分别使用支持向量机(Support Vector Machines,SVM)和BP(Back Propagation,BP)神经网络算法进行分类.实验结果表明,与使用单一的MFCC特征相比,BP神经网络对鸣笛声识别的有效率提高了10.4％,SVM的有效率提高了4.4％;相较于单一的GFCC特征,BP神经网络的有效率提高了6.6％,SVM的有效率提高了4.2％,证明了该融合特征能提高鸣笛声识别准确性.

Drug-target interactions (DTIs) prediction plays an important role in the process of drug discovery. Most computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks (SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI, which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions (DDIs) and protein-protein interactions (PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from DrugBank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

药物-靶标相互作用(DTI)预测在新药物研发中具有重要意义.大多数计算方法将其建模为二元预测问题,忽视了DTI的具体类型.考虑DTI的积极或消极作用,将有利于研究多种药物对共同靶标的综合作用机理.通过构建药物靶标符号网络,将DTI预测问题转化为药物与靶标异构网络的符号链路预测问题,并引入Logistic回归与随机游走构建学习系统.在两个数据集进行实验,其预测结果呈现出良好的指标,表明该思路的可行性.

药物-药物相互作用(Drug-drug interactions,DDIs)指病人在一定时间内服用两种及以上药物后药物产生的复合效应,可表现为药性增强或减弱.本文提出一种基于图神经网络模型的预测方法,在已有药物间相互作用基础上,结合药物化学结构特征等属性,分进行药物间相互作用预测实验.

Drug drug interactions (DDIs) are crucial for drug research and pharmacologia. Recently, graph neural networks (GNNs) have handled these interactions successfully and shown great predictive performance, but most computational approaches are built on an unsigned graph that commonly represents assortative relations between similar nodes. Semantic correlation between drugs, such as degressive effects or even adverse side reactions (ADRs), should be disassortative. This kind of DDIs networks can be represented as a signed graph taking drug profiles as node attributes, but negative edges have brought challenges to node embedding methods. We first propose a signed graph filtering-based convolutional network (SGFCN) for drug representations, which integrates both signed graph structures and drug profiles. Node features as graph signals are transited and aggregated with dedicated spectral filters that capture both assortativity and disassortativity of drug pairs. Furthermore, we put forward an end-to-end learning framework for DDIs, via training SGFCN together with a joint discriminator under a problem-specific loss function. Comparing with signed spectral embedding and graph convolutional networks, results on two prediction problems show SGFCN is encouraging in terms of metric indicators, and still achieves considerable level with a small-size model.

药物相互作用(DDI)是指两种或两种以上药物在药理行为方面的相互影响.大多数现有计算方法都是针对传统的二元预测而设计的,即确定DDI是否发生.然而,确定DDI是增强的还是抑制的,对于治疗和护理病人更具意义.受图神经网络启发,尝试利用符号图卷积网络来预测DDI的符号.将该问题转换为一个嵌入问题,然后通过对数几率回归获得DDI类型.结果表明,符号图卷积网络在该问题上表现良好,具有可行性.

子载波索引功率调制(SIPM)能够利用子载波索引信息调整所对应的功率信息,它应用于光纤-可见光融合系统中,可以更好地提高该系统的传输容量.提出了一种基于子载波索引功率调制-正交频分复用(SIPM-OFDM)的全双工光纤-可见光混合系统,利用波长重用方法简化了系统的结构,并建立了SIPM-0FDM信号的产生模型,通过仿真实验验证了系统的可行性.仿真结果表明:经过35 km标准单模光纤(SSMF)及5 m可见光双向传输之后,系统功率代价分别小于1 dB和2 dB,星座图依然清晰.