Due to the heterogeneity of cancer cells, personalized treatment plans for cancer patients remain a continuous concern. High-throughput drug screening technology has led to the development of deep learning models that generate personalized therapies. However, most existing models fail to account for the topological relationships between cell line-drug pair (CDP) nodes, thereby ignoring their intrinsic connections. This paper proposes a multi-channel hybrid graph convolutional neural network (MHGCN) for predicting cancer drug response (CDR). First, we define CDPs by integrating gene expression and drug molecular fingerprints. These CDPs are refined through denoising autoencoders to eliminate noise. Second, we compute pairwise cosine similarities among CDPs to build a similarity network, while simultaneously establishing a heterogeneous response graph connecting cell lines and drugs. Third, MHGCN processes the CDP network via graph convolutional layers and generates the response matrix through linear projection. Concurrently, a heterogeneous graph convolutional neural network learns the response heterogeneous network. Following data augmentation, we derive feature embeddings for cell lines and drugs, then compute their similarity matrix. Finally, CDR predictions are generated through weighted matrix fusion of these components. To the best of our knowledge, MHGCN represents the first framework explicitly incorporating CDP topology into CDR prediction. Experiments demonstrate MHGCN's statistically significant improvements over state-of-the-art methods.

Foucaud et al. recently introduced and initiated the study of a new graph-theoretic concept in the area of network monitoring. Given a graph G = ( V( G), E( G)), a set M ⊆ V( G) is a distance-edge-monitoring set if for every edge e ∈ E( G), there is a vertex x ∈ M and a vertex y ∈ V( G) such that the edge e belongs to all shortest paths between x and y. The smallest size of such a set in G is denoted by dem( G). Denoted by G – e (resp. G\ u) the subgraph of G obtained by removing the edge e from G (resp. a vertex u together with all its incident edges from G). In this paper, we first show that dem( G – e) – dem( G) ≤ 2 for any graph G and edge e ∈ E( G). Moreover, the bound is sharp. Next, we construct two graphs G and H to show that dem( G) – dem( G\ u) and dem( H \ v) – dem( H) can be arbitrarily large, where u ∈ V( G) and v ∈ V( H). We also study the relation between dem( H) and dem( G), where H is a subgraph of G. In the end, we give an algorithm to judge whether the distance-edge-monitoring set still remain in the resulting graph when any edge of a graph G is deleted.

To balance the inference speed and detection accuracy of a grasp detection algorithm, which are both important for robot grasping tasks, we propose an encoder–decoder structured pixel-level grasp detection neural network named the attention-based efficient robot grasp detection network (AE-GDN). Three spatial attention modules are introduced in the encoder stages to enhance the detailed information, and three channel attention modules are introduced in the decoder stages to extract more semantic information. Several lightweight and efficient DenseBlocks are used to connect the encoder and decoder paths to improve the feature modeling capability of AE-GDN. A high intersection over union (IoU) value between the predicted grasp rectangle and the ground truth does not necessarily mean a high-quality grasp configuration, but might cause a collision. This is because traditional IoU loss calculation methods treat the center part of the predicted rectangle as having the same importance as the area around the grippers. We design a new IoU loss calculation method based on an hourglass box matching mechanism, which will create good correspondence between high IoUs and high-quality grasp configurations. AEGDN achieves the accuracy of 98.9% and 96.6% on the Cornell and Jacquard datasets, respectively. The inference speed reaches 43.5 frames per second with only about 1.2 × 106 parameters. The proposed AE-GDN has also been deployed on a practical robotic arm grasping system and performs grasping well. Codes are available at https://github.com/robvincen/robot_gradet .

BACKGROUND: Drug-Drug interactions (DDIs) are a challenging problem in drug research. Drug combination therapy is an effective solution to treat diseases, but it can also cause serious side effects. Therefore, DDIs prediction is critical in pharmacology. Recently, researchers have been using deep learning techniques to predict DDIs. However, these methods only consider single information of the drug and have shortcomings in robustness and scalability. RESULTS: In this paper, we propose a multi-type feature fusion based on graph neural network model (MFFGNN) for DDI prediction, which can effectively fuse the topological information in molecular graphs, the interaction information between drugs and the local chemical context in SMILES sequences. In MFFGNN, to fully learn the topological information of drugs, we propose a novel feature extraction module to capture the global features for the molecular graph and the local features for each atom of the molecular graph. In addition, in the multi-type feature fusion module, we use the gating mechanism in each graph convolution layer to solve the over-smoothing problem during information delivery. We perform extensive experiments on multiple real datasets. The results show that MFFGNN outperforms some state-of-the-art models for DDI prediction. Moreover, the cross-dataset experiment results further show that MFFGNN has good generalization performance. CONCLUSIONS: Our proposed model can efficiently integrate the information from SMILES sequences, molecular graphs and drug-drug interaction networks. We find that a multi-type feature fusion model can accurately predict DDIs. It may contribute to discovering novel DDIs.

BACKGROUND: Protein-protein interactions are widespread in biological systems and play an important role in cell biology. Since traditional laboratory-based methods have some drawbacks, such as time-consuming, money-consuming, etc., a large number of methods based on deep learning have emerged. However, these methods do not take into account the long-distance dependency information between each two amino acids in sequence. In addition, most existing models based on graph neural networks only aggregate the first-order neighbors in protein-protein interaction (PPI) network. Although multi-order neighbor information can be aggregated by increasing the number of layers of neural network, it is easy to cause over-fitting. So, it is necessary to design a network that can capture long distance dependency information between amino acids in the sequence and can directly capture multi-order neighbor information in protein-protein interaction network. RESULTS: In this study, we propose a multi-hop neural network (LDMGNN) model combining long distance dependency information to predict the multi-label protein-protein interactions. In the LDMGNN model, we design the protein amino acid sequence encoding (PAASE) module with the multi-head self-attention Transformer block to extract the features of amino acid sequences by calculating the interdependence between every two amino acids. And expand the receptive field in space by constructing a two-hop protein-protein interaction (THPPI) network. We combine PPI network and THPPI network with amino acid sequence features respectively, then input them into two identical GIN blocks at the same time to obtain two embeddings. Next, the two embeddings are fused and input to the classifier for predict multi-label protein-protein interactions. Compared with other state-of-the-art methods, LDMGNN shows the best performance on both the SHS27K and SHS148k datasets. Ablation experiments show that the PAASE module and the construction of THPPI network are feasible and effective. CONCLUSIONS: In general terms, our proposed LDMGNN model has achieved satisfactory results in the prediction of multi-label protein-protein interactions.

Abstract Recently, graph convolutional networks have shown excellent results in skeleton‐based action recognition. This paper presents a multi‐stage part‐aware graph convolutional network for the problems of model over complication, parameter redundancy and lack of long‐dependence feature information. The structure of this network has a multi‐stream input and two‐stream output, which can greatly reduce the complexity and improve the accuracy of the model without losing sequence information. The two branches of the network have the same backbone, which includes 6 multi‐order feature extraction blocks and 3 temporal attention calibration blocks, and the outputs of the two branches are fused together. In multi‐order feature extraction block, a channel‐spatial attention mechanism and a graph condensation module are proposed, which can extract more distinguishable feature and identify the relationship between parts. In temporal attention calibration block, the temporal dependencies between frames in the skeleton sequence are modeled. Experimental results show that the proposed network outperforms many mainstream methods on NTU and Kinetics datasets, for example, it achieves 92.4% accuracy on the cross‐subject benchmark of NTU‐RGBD60 dataset.

Designing a novel and efficient architecture is the thrust of medical image segmentation. In this paper, we introduce a novel network named Multi-scale Feedback Feature Refinement U-Net (MFFRU-Net) for medical image segmentation. We design a simple and effective multi-scale feedback structure. Up-sampling and 1 × 1 convolution are used to feedback the feature maps of different scales in the decoder to the encoder, so that multiple high-level and low-level features are fused to obtain more representative features. Specifically, we propose a feature refinement module (FRM) based on the dual attention mechanism in the middle layer of the network. FRM block can enhance the use of spatial and channel information of image features. We evaluate the MFFRU-Net on two datasets. Comprehensive experimental results show that the proposed method is superior to the original U-Net method and other advanced methods.