Predicting new drug–target interactions is an important step in new drug development, understanding of its side effects and drug repositioning. Heterogeneous data sources can provide comprehensive information and different perspectives for drug–target interaction prediction. Thus, there have been many calculation methods relying on heterogeneous networks. Most of them use graph-related algorithms to characterize nodes in heterogeneous networks for predicting new drug–target interactions (DTI). However, these methods can only make predictions in known heterogeneous network datasets, and cannot support the prediction of new chemical entities outside the heterogeneous network, which hinder further drug discovery and development. To solve this problem, we proposed a multi-modal DTI prediction model named ‘MultiDTI’ which uses our proposed joint learning framework based on heterogeneous networks. It combines the interaction or association information of the heterogeneous network and the drug/target sequence information, and maps the drugs, targets, side effects and disease nodes in the heterogeneous network into a common space. In this way, ‘MultiDTI’ can map the new chemical entity to this learned common space based on the chemical structure of the new entity. That is, bridging the gap between new chemical entities and known heterogeneous network. Our model has strong predictive performance, and the area under the receiver operating characteristic curve of the model is 0.961 and the area under the precision recall curve is 0.947 with 10-fold cross validation. In addition, some predicted new DTIs have been confirmed by ChEMBL database. Our results indicate that ‘MultiDTI’ is a powerful and practical tool for predicting new DTI, which can promote the development of drug discovery or drug repositioning. Python codes and dataset are available at https://github.com/Deshan-Zhou/MultiDTI/. Supplementary data are available at Bioinformatics online.

Major Depressive Disorder (MDD) is an affective disorder that can lead to persistent sadness and a decline in the quality of life, increasing the risk of suicide. Utilizing multimodal data such as electroencephalograms and patient interview audios can facilitate the timely detection of MDD. However, existing depression detection methods either consider only a single modality or do not fully account for the differences and similarities between modalities in multimodal approaches, potentially overlooking the latent information inherent in various modal data. To address these challenges, we propose EMO-GCN, a multimodal depression detection method based on an adaptive multi-graph neural network. By employing graph-based methods to model data from various modalities and extracting features from them, the potential correlations between modalities are uncovered. The model’s performance on the MODMA dataset is outstanding, achieving an accuracy (ACC) of 96.30%. Ablation studies further confirm the effectiveness of the model’s individual components.The experimental results of EMO-GCN demonstrate the application prospects of graph-based multimodal analysis in the field of mental health, offering new perspectives for future research.

MicroRNAs (miRNAs) play a crucial role in the prevention, prognosis, diagnosis, and treatment of complex diseases. Existing computational methods primarily focus on biologically relevant molecules directly associated with miRNA or disease, overlooking the fact that the human body is a highly complex system where miRNA or disease may indirectly correlate with various types of biomolecules. To address this, we propose a novel prediction model named MHGTMDA (miRNA and disease association prediction using heterogeneous graph transformer based on molecular heterogeneous graph). MHGTMDA integrates biological entity relationships of eight biomolecules, constructing a relatively comprehensive heterogeneous biological entity graph. MHGTMDA serves as a powerful molecular heterogeneity map transformer, capturing structural elements and properties of miRNAs and diseases, revealing potential associations. In a 5-fold cross-validation study, MHGTMDA achieved an area under the receiver operating characteristic curve of 0.9569, surpassing state-of-the-art methods by at least 3%. Feature ablation experiments suggest that considering features among multiple biomolecules is more effective in uncovering miRNA-disease correlations. Furthermore, we conducted differential expression analyses on breast cancer and lung cancer, using MHGTMDA to further validate differentially expressed miRNAs. The results demonstrate MHGTMDA's capability to identify novel MDAs.

MicroRNAs (miRNAs) synergize with various biomolecules in human cells resulting in diverse functions in regulating a wide range of biological processes. Predicting potential disease-associated miRNAs as valuable biomarkers contributes to the treatment of human diseases. However, few previous methods take a holistic perspective and only concentrate on isolated miRNA and disease objects, thereby ignoring that human cells are responsible for multiple relationships. In this work, we first constructed a multi-view graph based on the relationships between miRNAs and various biomolecules, and then utilized graph attention neural network to learn the graph topology features of miRNAs and diseases for each view. Next, we added an attention mechanism again, and developed a multi-scale feature fusion module, aiming to determine the optimal fusion results for the multi-view topology features of miRNAs and diseases. In addition, the prior attribute knowledge of miRNAs and diseases was simultaneously added to achieve better prediction results and solve the cold start problem. Finally, the learned miRNA and disease representations were then concatenated and fed into a multi-layer perceptron for end-to-end training and predicting potential miRNA–disease associations. To assess the efficacy of our model (called MUSCLE), we performed 5- and 10-fold cross-validation (CV), which got average the Area under ROC curves of 0.966${\pm }$0.0102 and 0.973${\pm }$0.0135, respectively, outperforming most current state-of-the-art models. We then examined the impact of crucial parameters on prediction performance and performed ablation experiments on the feature combination and model architecture. Furthermore, the case studies about colon cancer, lung cancer and breast cancer also fully demonstrate the good inductive capability of MUSCLE. Our data and code are free available at a public GitHub repository: https://github.com/zht-code/MUSCLE.git.

This research presents a “flexible support structure between reflective mirrors” through a coupling analysis method to restrain the surface shaping error of reflectors in the optical system of airborne LiDAR bathymetry (ALB) under various working conditions. The flexible structure proposed adjusts the mechanical relationship between the reflectors and the support structure to reduce reflector mirror deformation. The optical system is first modeled using Zemax and exported to SolidWorks to create a 3D model of the optical receiving system. Ansys is then used to conduct stiffness testing and surface analysis on the support structure of the annular thin cylinder. According to the analysis results, the first-order frequency of the support structure using a ring-shaped thin cylinder is as high as 353.64 Hz, which indicates that it has good dynamic characteristics. The PV value of the reflector mirror deformation under the thermal coupling reaches 32.59 nm, and the RMS value reaches 8.62 nm. Additionally, it is discovered that the maximum acceleration response of the reflector mirror under the applied 1 g acceleration excitation reaches 4.22 g when carrying out the dynamics analysis of the support structure. Under random vibration analysis, the maximum acceleration RMS value of the reflector mirror assembly reaches 2.18 g, and the maximum stress of the flexible device of the support structure reaches 2.65 MPa. Especially, five groups of experimental results demonstrated that the proposed coupling analysis method can receive the echo signals, the reflector mirror support structure designed in this paper, and the flexible structure is stable and reliable.

The core task of target detection is to accurately identify and localize the object of interest from a multitude of interfering factors. This task is particularly difficult in UAV aerial images, where targets are often small and the background can be extremely complex. In response to these challenges, this study introduces an enhanced target detection algorithm for UAV aerial images based on the YOLOv7-tiny network. In order to enhance the convolution module in the backbone of the network, the Receptive Field Coordinate Attention Convolution (RFCAConv) in place of traditional convolution enhances feature extraction within critical image regions. Furthermore, the tiny target detection capability is effectively enhanced by incorporating a tiny object detection layer. Moreover, the newly introduced BSAM attention mechanism dynamically adjusts attention distribution, enabling precise target–background differentiation, particularly in cases of target similarity. Finally, the innovative inner-MPDIoU loss function replaces the CIoU, which enhances the sensitivity of the model to changes in aspect ratio and greatly improves the detection accuracy. Experimental results on the VisDrone2019 dataset reveal that relative to the YOLOv7-tiny model, the improved YOLOv7-tiny model improves precision (P), recall (R), and mean average precision (mAP) by 4.1%, 5.5%, and 6.5%, respectively, thus confirming the algorithm’s superiority over existing mainstream methods.