SAGCN: Using graph convolutional network with subgraph-aware for circRNA-drug sensitivity identification.

Circular RNAs (circRNAs) play a significant role in cancer development and therapy resistance. There is substantial evidence indicating that the expression of circRNAs affects the sensitivity of cells to drugs. Identifying circRNAs-drug sensitivity association (CDA) is helpful for disease treatment and drug discovery. However, the identification of CDA through conventional biological experiments is both time-consuming and costly. Therefore, it is urgent to develop computational methods to predict CDA. In this study, we propose a new computational method, the subgraph-aware graph convolutional network (SAGCN), for predicting CDA. SAGCN first construct a heterogeneous network composed of circRNA similarity network, drug similarity network, and circRNA-drug bipartite network. Then, a subgraph extractor is proposed to learn the latent subgraph structure of the heterogeneous network using a graph convolutional network. The extractor can capture 1-hop and 2-hop information and then a fusing attention mechanism is designed to integrate them adaptively. Simultaneously, a novel subgraph-aware attention mechanism is proposed to detect intrinsic subgraph structure. The final node feature representation is obtained to make the CDA prediction. Experimental results demonstrate that SAGCN obtained an average AUC of 0.9120 and AUPR of 0.8693, exceeding the performance of the most advanced models under 10-fold cross-validation. Case studies have demonstrated the potential of SAGCN in identifying associations between circRNA and drug sensitivity.

A novel approach for automatic segmentation of prostate and its lesion regions on magnetic resonance imaging.

Objective: To develop an accurate and automatic segmentation model based on convolution neural network to segment the prostate and its lesion regions. Methods: Of all 180 subjects, 122 healthy individuals and 58 patients with prostate cancer were included. For each subject, all slices of the prostate were comprised in the DWIs. A novel DCNN is proposed to automatically segment the prostate and its lesion regions. This model is inspired by the U-Net model with the encoding-decoding path as the backbone, importing dense block, attention mechanism techniques, and group norm-Atrous Spatial Pyramidal Pooling. Data augmentation was used to avoid overfitting in training. In the experimental phase, the data set was randomly divided into a training (70%), testing set (30%). four-fold cross-validation methods were used to obtain results for each metric. Results: The proposed model achieved in terms of Iou, Dice score, accuracy, sensitivity, 95% Hausdorff Distance, 86.82%,93.90%, 94.11%, 93.8%,7.84 for the prostate, 79.2%, 89.51%, 88.43%,89.31%,8.39 for lesion region in segmentation. Compared to the state-of-the-art models, FCN, U-Net, U-Net++, and ResU-Net, the segmentation model achieved more promising results. Conclusion: The proposed model yielded excellent performance in accurate and automatic segmentation of the prostate and lesion regions, revealing that the novel deep convolutional neural network could be used in clinical disease treatment and diagnosis.

Bridging-BPs: a novel approach to predict potential drug-target interactions based on a bridging heterogeneous graph and BPs2vec.

Predicting drug-target interactions (DTIs) is a convenient strategy for drug discovery. Although various computational methods have been put forward in recent years, DTIs prediction is still a challenging task. In this paper, based on indirect prior information (we term them as mediators), we proposed a new model, called Bridging-BPs (bridging paths), for DTIs prediction. Specifically, we regarded linkage process between mediators and DTs (drugs and proteins) as 'bridging' and source (drug)-mediators-destination (protein) as bridging paths. By integrating various bridging paths, we constructed a bridging heterogeneous graph for DTIs. After that, an improved graph-embedding algorithm-BPs2vec-was designed to capture deep topological features underlying the bridging graph, thereby obtaining the low-dimensional node vector representations. Then, the vector representations were fed into a Random Forest classifier to train and score the probability, outputting the final classification results for potential DTIs. Under 5-fold cross validation, our method obtained AUPR of 88.97% and AUC of 88.63%, suggesting that Bridging-BPs could effectively mine the link relationships hidden in indirect prior information and it significantly improved the accuracy and robustness of DTIs prediction without direct prior information. Finally, we confirmed the practical prediction ability of Bridging-BPs by case studies.

Coastal Waste Detection Based on Deep Convolutional Neural Networks.

Coastal waste not only has a seriously destructive effect on human life and marine ecosystems, but it also poses a long-term economic and environmental threat. To solve the issues of a poor manual coastal waste sorting environment, such as low sorting efficiency and heavy tasks, we develop a novel deep convolutional neural network by combining several strategies to realize intelligent waste recognition and classification based on the state-of-the-art Faster R-CNN framework. Firstly, to effectively detect small objects, we consider multiple-scale fusion to get rich semantic information from the shallower feature map. Secondly, RoI Align is introduced to solve positioning deviation caused by the regions of interest pooling. Moreover, it is necessary to correct key parameters and take on data augmentation to improve model performance. Besides, we create a new waste object dataset, named IST-Waste, which is made publicly to facilitate future research in this field. As a consequence, the experiment shows that the algorithm's mAP reaches 83%. Detection performance is significantly better than Faster R-CNN and SSD. Thus, the developed scheme achieves higher accuracy and better performance against the state-of-the-art alternative.