Single-cell multi-omics sequencing represents an advanced technology capable of simultaneously measuring multiple omics data from the same cell. The joint clustering of single-cell multi-omics sequencing data enables a comprehensive depiction of cell states and uncovers intricate molecular mechanisms, holding immense significance in fields such as oncology, neurology, and developmental biology. However, the disparities in feature spaces across different omics layers and data noise present substantial challenges for achieving accurate clustering. To tackle these challenges, we introduce a novel clustering method for single-cell multi-omics data, termed scMSAC, which is grounded in a denoising subgraph attention autoencoder. The proposed method employs a weighted nearest neighbor graph strategy to ascertain the weights of multi-omics data, subsequently generating a similarity graph that holistically encapsulates intercellular connections through the weighted amalgamation of diverse omics perspectives. The scMSAC model captures the topological features of cells through the subgraph attention autoencoder, constructing relationships among cells. For the omics features extracted by the subgraph attention autoencoder, scMSAC incorporates an SCA (Spatial Channel Attention) mechanism for feature fusion to reduce the differences in feature spaces of different omics and achieve better clustering performance. Comparative experiments with various existing methods demonstrate that scMSAC has excellent clustering performance and performs well in detecting rare cell types and differential expression analysis.

Graph transformer networks have received more attention in hyperspectral image (HSI) classification. However, they overlooked the influence of graph connectivity strength in positional encoding and distribution. In order to address the above deficiencies, we proposed the novel graph transformer with structural embedding and training (GTSET) for HSI classification. Specifically, the structural embedding module firstly aimed at extracting effectively local and non-local feature information via patch-based distance encoding and centrality correlation coefficients based on graph connectivity strength, alleviating spectral variability. Secondly, the structural training module aimed at addressing imbalanced structural position distribution of labeled samples by leveraging the topological graph connectivity to determine their structural position distribution and reweighting the influence of labeled samples on the graph transformer training stage, exploring the guiding role of labeled samples in low spatial resolution of HSI. Next, we further refine training weights based on the spectral feature smoothness of labeled samples. Finally, comprehensive experiments on three real-world HSI datasets demonstrate that the GTSET achieves superior performance in HSI classification with limited labeled samples, compared to other popular classification methods. Implementation of GTSET, along with examples, can be found on the GitHub repository: https://github.com/xuchengchao0/GTSET.

Single-cell RNA sequencing (scRNA-seq) enables the reconstruction of cell type-specific gene regulatory networks (GRNs), offering detailed insights into gene regulation at high resolution. While graph neural networks have become widely used for GRN inference, their message-passing mechanisms are often limited by issues such as over-smoothing and over-squashing, which hinder the preservation of essential network structure. To address these challenges, we propose a novel graph transformer-based model, AttentionGRN, which leverages soft encoding to enhance model expressiveness and improve the accuracy of GRN inference from scRNA-seq data. Furthermore, the GRN-oriented message aggregation strategies are designed to capture both the directed network structure information and functional information inherent in GRNs. Specifically, we design directed structure encoding to facilitate the learning of directed network topologies and employ functional gene sampling to capture key functional modules and global network structure. Our extensive experiments, conducted on 88 datasets across two distinct tasks, demonstrate that AttentionGRN consistently outperforms existing methods. Furthermore, AttentionGRN has been successfully applied to reconstruct cell type-specific GRNs for human mature hepatocytes, revealing novel hub genes and previously unidentified transcription factor-target gene regulatory associations.

BACKGROUND: The progression of cancer is driven by the accumulation of mutations in driver genes. Many researches promote to identify cancer driver genes. However, most of them ignore the high-order features in the network. RESULT: In this study, we propose a novel method MLGCN-Driver based on multi-layer graph convolutional neural networks (GCN) to boost driver gene identification. MLGCN-Driver employs multi-layer GCN with initial residual connections and identity mappings to learn biological multi-omics features within biological networks. In addition, node2vec algorithm is used to extract the topological structure features of the biological network, and then the features are fed into another multi-layer GCN for feature learning. Meanwhile, the initial residual connections and identity mappings mitigate the over-smooth of features. Finally, the probability of each gene being a driver gene is calculated based on low-dimensional biological features and topological features. CONCLUSION: We applied the MLGCN-Driver on pan-cancer dataset and cancer type-specific datasets. Experimental results demonstrate the excellent performance of MLGCN-Driver in terms of the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPRC) when compared with state-of-the-art approaches.

Classification methods based on subgraph neural networks (SNNs) are rarely explored, and its advantage is that it can alleviate the neighbor explosion problem. After applying SNNs to hyperspectral image (HSI) classification, the imbalanced topology structure in the internal subgraph leads to poor classification performance due to the intraclass and interclass spectral feature variation. Based on this, we proposed a novel subgraph-dependent neural network (SGDNet) for HSI classification. Specifically, we firstly segmented the large graph to a series of subgraphs, and proposed a subgraph-dependent convolution method for imbalanced subgraph structure to achieve effective feature smoothing within each subgraph. It mainly utilized the degree feature embedding with the residual to improve the feature diversity as well as the intraclass distance of clustering measurement to determine the optimal subgraph convolution layers in a feedback way. Secondly, we developed the strategy of the long-range dependency to address the inevitably local structure dependence among nodes within subgraphs. It utilized an anchor point based position coding method to capture the relative positions of unlabeled nodes in relation to all labeled nodes within the graph and further constructed the structural loss function for effective training, achieving subgraph optimization. Comprehensive experiments demonstrated the superior performance of the SGDNet model on three publicly available HSI datasets, compared to other popular methods. The source code will be available at https://github.com/lichao226211/SGDNet.

Intracranial aneurysm (IA) is a vascular disease of the brain arteries caused by pathological vascular dilation, which can result in subarachnoid hemorrhage if ruptured. Automatically classification and segmentation of intracranial aneurysms are essential for their diagnosis and treatment. However, the majority of current research is focused on two-dimensional images, ignoring the 3D spatial information that is also critical. In this work, we propose a novel dual-branch fusion network called the Point Cloud and Multi-View Medical Neural Network (PMMNet) for IA classification and segmentation. Specifically, one branch based on 3D point clouds serves the purpose of extracting spatial features, whereas the other branch based on multi-view images acquires 2D pixel features. Ultimately, the two types of features are fused for IA classification and segmentation. To extract both local and global features from 3D point clouds, Multilayer Perceptron (MLP) and the attention mechanism are used in parallel. In addition, a SPSA module is proposed for multi-view image feature learning, which extracts more exquisite channel and spatial multi-scale features from 2D images. Experiments conducted on the IntrA dataset outperform other state-of-the-art methods, demonstrating that the proposed PMMNet exhibits strong superiority on the medical 3D dataset. We also obtain competitive results on public datasets, including ModelNet40, ModelNet10, and ShapeNetPart, which further validate the robustness and generality of the PMMNet.

Segmentation of thoracic and abdominal organs is crucial for accurate disease diagnosis, surgical planning, and long-term health management of patients. Deep learning often depends on large quantities of high-quality training data to achieve superior results. However, the inherent complexity and sparsity of medical images require advanced models with greater learning capabilities. Additionally, significant morphological differences between organs often result in inaccurate and false-positive segmentation. To alleviate these issues, we propose a Dual Attention (DA) and Multi-Resolution Inputs (MI) based network (DAMNet) for the segmentation of thoracic and abdominal organs. Specially, DAMNet utilizes a single encoder and dual decoders. The encoder integrates MI with the Transformer to achieve multi-level feature fusion and capture global image relationships, improving the model’s capability to process intricate image data. In the decoders, Residual U-blocks (RSU) and the DA module consisting of Spatial Multi-Scale Cross-Axis Attention (SMCA) and Convolutional Self-Attention (CSA) are used as two decoder branches, respectively. The design of decoders allows the model to extract detailed information from different encoding layers from two perspectives, thereby reducing inaccuracies in segmentation. We perform thorough experiments and evaluations using three publicly available datasets: Synapse, SegTHOR, and THoracic. The experimental results indicate that our proposed DAMNet model demonstrates exceptional proficiency in segmenting thoracic and abdominal organs.

The inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addition, the GRNs of real species are always large graphs with direction and high sparsity, which impede the advancement of GRN inference. Therefore, DeepFGRN builds a node bidirectional representation module to capture the directed graph embedding representation of the GRN. Specifically, the source and target generators are designed to learn the low-dimensional dense embedding of the source and target neighbors of a gene, respectively. An adversarial learning strategy is applied to iteratively learn the real neighbors of each gene. In addition, because the expression profiles of genes with regulatory associations are correlative, a correlation analysis module is designed. Specifically, this module not only fully extracts gene expression features, but also captures the correlation between regulators and target genes. Experimental results show that DeepFGRN has a competitive capability for both GRN and FGRN inference. Potential biomarkers and therapeutic drugs for breast cancer, liver cancer, lung cancer and coronavirus disease 2019 are identified based on the candidate FGRNs, providing a possible opportunity to advance our knowledge of disease treatments.

Graph convolutional networks (GCNs)-based methods for hyperspectral image (HSI) classification have received more attention due to its flexibility in information aggregation. However, most existing GCN-based methods in HSI community rely on capturing fixed K-hops neighbors for feature information aggregation, which ignores the inherent imbalance in class distributions and fails to achieve optimal feature smoothing through graph convolution operator. It is unreasonable to apply fixed K-hops strategy for feature smoothing in imbalanced classes, as class regions with rich contextual information and those with poor contextual information require to capture different hops neighbors to achieve the optimal feature smoothing. To address this issue, this article proposes a novel approach called class-imbalanced graph convolution smoothing (CIGCS) for HSI classification, which achieves adaptive feature smoothing for imbalanced class regions. Firstly, we construct a semantic block-diagonal graph structure that describes imbalanced semantic class regions by considering label connectivity and spectral Laplacian regularizer. Secondly, we develop the class-imbalanced graph convolution smoothing technique to adaptively aggregate neighbor information for imbalanced class regions based on the decreasing Euclidean distance of samples within each bock-diagonal structure from the perspective of over-smoothing. The choice of adaptive neighbors can be guaranteed by a theoretical upper bound. Finally, the obtained optimal smoothed features are fed into the logistic regression to achieve good classification results. The proposed CIGCS method is evaluated on three real HSI data sets to demonstrate its superiority compared to some popular GCN-based methods.

Researchers have been paying more attention to hyperspectral image (HSI) classification based on semi-supervised superpixel-level graph convolutional networks (SGCNs) due to their aggregation ability of rich contextual information. Although these SGCNs achieve good classification performance, the influence of the positional distributions among labeled superpixels has been overlooked. The locations of labeled superpixels, such as located at class boundaries or centers, exert a substantial influence on the final performance. To address this issue, this article proposed a novel graph neural network (GCN) method with the guidance of positional distributions of labeled superpixels, abbreviated as LPDGCN. Specifically, we first propose to utilize the sparse, low-rank as well as feature smoothness restrictions to optimize the initial superpixel graph structure because the connectivity relationships of labeled superpixels located at class boundaries or centers are easily influenced by spectral variation. Second, in order to effectively determine the positional distributions of labeled superpixels and make full use of the position relationships, we propose to utilize the information conflict from the above topology connectivity to determine the positional distributions of labeled superpixels and develop the reweighted strategy to weaken the influence of labeled superpixels located at class boundaries and strengthen the influence of that located at class centers. Finally, we evaluate the LPDGCN method on four public HSI datasets, demonstrating its superiority over other advanced classification methods in terms of three metrics, i.e., overall accuracy (OA), average accuracy (AA), and kappa coefficient (KC).

Antiviral peptides (AVPs) are widely found in animals and plants, with high specificity and strong sensitivity to drug-resistant viruses. However, due to the great heterogeneity of different viruses, most of the AVPs have specific antiviral activities. Therefore, it is necessary to identify the specific activities of AVPs on virus types. Most existing studies only identify AVPs, with only a few studies identifying subclasses by training multiple binary classifiers. We develop a two-stage prediction tool named FFMAVP that can simultaneously predict AVPs and their subclasses. In the first stage, we identify whether a peptide is AVP or not. In the second stage, we predict the six virus families and eight species specifically targeted by AVPs based on two multiclass tasks. Specifically, the feature extraction module in the two-stage task of FFMAVP adopts the same neural network structure, in which one branch extracts features based on amino acid feature descriptors and the other branch extracts sequence features. Then, the two types of features are fused for the following task. Considering the correlation between the two tasks of the second stage, a multitask learning model is constructed to improve the effectiveness of the two multiclass tasks. In addition, to improve the effectiveness of the second stage, the network parameters trained through the first-stage data are used to initialize the network parameters in the second stage. As a demonstration, the cross-validation results, independent test results and visualization results show that FFMAVP achieves great advantages in both stages.

Hyperspectral image classification methods based on the graph convolutional network (GCN) have received more attention because they can handle irregular regions by graph encoding techniques. However, GCN-based HSI classification methods are highly sensitive to the quality of the graph structure. Its performance degrades in the case of underdeveloped graphs because it cannot excavate the intrinsic adjacency relationships. Thus, it is necessary to improve the quality of graph structure in GCN-based methods. In this paper, a novel diversity-connected graph convolutional network (DCGCN) method is proposed to improve the quality of the graph structure for HSI classification, and its basic idea can be adopted by other GCN-based methods. First, the potential neighbors are excavated by performing topological extensions based on the given graph. The diversity of surrounding neighbors is maintained by adaptively smoothing operation via a global threshold value from Kullback-Leibler divergence to eliminate weak interclass connections caused by weakly spectral variability. Second, another key connectivity restriction is imposed on the diverse neighbors to further refine the ambiguous connections of hard samples aiming at removing strong interclass connections where the spectral information is heavily confounded. Finally, the DCGCN method is analyzed theoretically to demonstrate its low-pass filter property. The comprehensive experiments demonstrate the effectiveness of the proposed DCGCN method and the basic idea of the diversity-connected graph in terms of overall accuracy (OA), kappa coefficient (KC), average accuracy (AA) indexes.

Although graph convolutional network (GCN) has achieved remarkable success in hyperspectral image (HSI) classification, most existing GCN-based approaches have failed to realize a deep network structure due to the oversmoothing problem. This problem largely limits the expression ability and feature extraction ability of GCN and hampers GCN’s capacity to model long-range relationships between samples in hyperspectral (HS) scenes. Moreover, there is a lack of theoretical analysis in those works that constructed deep GCN for HSI classification to illustrate how they overcome the oversmoothing problem. Aside from this, the characteristics and complexity of HSI are often neglected when constructing deep GCN models in HSI classification. To address these problems, a novel deep graph network based on first-order smoothing is proposed for HSI classification. Specifically, a local and global topologically consistent graph is constructed to thoroughly explore the union between fine pixel information and semantic superpixel information. Subsequently, a novel propagation procedure is proposed to address the oversmoothing problem. We creatively build a residual connection to the first layer to emphasize the feature information aggregated from the first-order neighborhood, which adds node features that have not yet become indistinguishable into deep layer, and at the same time, it can be considered as a correction to the original pixels affected by spectral variation in the input graph. Finally, we demonstrate how first-order smoothing-based deep graph network (FSDGN) can slow down the convergence rate of the oversmoothing problem by analyzing the propagation of FSDGN from the standpoint of the Laplacian spectral domain. In addition, the results of experiments performed on three benchmark datasets demonstrate its superiority over other state-of-the-art methods.

In hyperspectral image (HSI) classification task, semisupervised graph convolutional network (GCN)-based methods have received increasing attention. However, two problems still need to be addressed. The first is that the initial graph structure in the GCN-based methods is not sufficiently flexible to encode the homogenous structure similarity of HSI pixels when facing the complex scenarios induced by the spatial variability. Another problem is that the input (graph structure) and output (output features) of the GCN-based methods are separated with a "single pass" procedure, which is a suboptimal problem for HSI classification because it does not flexibly optimize the graph construction with a feedback method via output features. In this article, a novel spatial-spectral unified adaptive probability GCN (SSAPGCN) method is proposed for HSI classification. First, considering the homogeneous structural similarity of the pairwise relationships of HSI pixels, this article combines the inherent spectral information and spatial coordinates to obtain the spatial-spectral adaptive probability graph (SSAPG) structure, which can capture the probabilistic connectivity between each pair of the homogeneous HSI pixels. Second, the SSAPG structure and GCN model are combined into a unified framework to a daptively learn both the graph structure and the output features simultaneously with feedback. Finally, the proposed SSAPGCN method with two layers is evaluated on four public HSI datasets to demonstrate its superiority over different classification methods in terms of two evaluation metrics, the overall accuracy (OA) and kappa coefficient (KC), especially with small training sample sizes.

Graph convolutional networks (GCNs) have been shown to be effective for hyperspectral image (HSI) classification due to their capacity to learn representations of spatial–spectral features. However, the existing GCN-based models heavily rely on predefined receptive fields to capture and aggregate neighbor information for each node, which limits the ability to adaptively selecting the most significant receptive field from graph data. To address the aforementioned problem, in this article, we propose a novel dynamic adaptive sampling GCN (DAS-GCN) algorithm that captures neighbor information through adaptive sampling to allow the receptive field to be dynamically obtained. The basic underlying idea is that the most meaningful receptive field for each target node can be adaptively discovered, and the edge adjacency weights can be adjusted simultaneously after each adaptive sampling operation. Thus, we enable the graph to be dynamically updated and refined. Specifically, the adaptive sampling operation consists of two complementary components; in the first step, the importance of different remote nodes in a large-scale neighborhood is learned, while in the second step, rich underlying spatial–spectral information is extracted from local neighbors and filtered. The proposed model has the ability to learn how to extensively exploit spectral–spatial correlations from both local and remote nodes. Moreover, the proposed DAS-GCN model has a superior ability to leverage node feature information to naturally generalize and efficiently generate node embeddings for unseen data. The experimental results with overall accuracy on four real HSI datasets, i.e., Indian Pines, Pavia university, Houston 2013, and Salinas are 95.63%, 96.40%, 94.70%, and 99.08%, respectively, which clearly demonstrate the advantages of the proposed method compared with other state-of-the-art approaches.

Generally, the apparent spectral information of hyperspectral images (HSIs) is directly used to measure the similarity among HSI pixels in the feature space, but this process cannot reveal the inherent characteristics of HSI pixels. Moreover, constructing spatial-spectral block-diagonal subspace structure representations of intraclass land-cover samples remains a challenge for low-rank representation (LRR) in HSI classification. In this article, we propose two methods to reveal the complex intrinsic spatial-spectral features of HSIs using block-diagonal subspace structures, namely, the spatial-spectral block-diagonal structure representation with class probability (SSBDCP) and spatial-spectral block-diagonal structure representation with fuzzy class probability (SSBDFCP) methods, for HSI classification. First, the SSBDFCP and SSBDCP methods explore the structure similarity characteristics of the latent subspace to form a block-diagonal LRR (BDLRR) of intraclass pixels with class probability and fuzzy class probability (FCP) and suppress the interclass pixels' representations. Then, the spatial information is considered in the proposed methods to enhance spatial-spectral graph expression and capture more comprehensive information. Note that SSBDFCP can perform better than SSBDCP because the FCP considers the “typicalness” that a sample belongs to a specific category and utilizes complex intrinsic discriminative information based on the feedback of “weakly” supervised information. Moreover, the feedback information can remove the noise features around the pixels and take advantage of the benefits of true neighbors. The experimental results for the Indian Pines and Pavia University data sets show that the SSBDFCP and SSBDCP methods achieve better HSI classification results than other popular graph construction methods.

There have been many ways to construct an algorithm to encrypt image. Most often the algorithms are based on DNA sequence or other methods. In this paper, we proposed a new method which is based on singular value decomposition. In this approach, we can encrypt a small portion of the data through RSA encryption algorithm. The strength of the proposed method is insured through various statistical and security analysis. It shows that the algorithm has good encryption effect and higher encryption efficiency, which can be applied to the storage and network transmission of military, medical and other digital images.

We propose a novel model-driven application-level encryption solution to protect the privacy and confidentiality of health data in response to the growing public concern about the privacy of health data. Domain experts specify sensitive data which are to be protected by encryption in the application's domain model. Security experts specify the cryptographic parameters used for the encryption in a security configuration. Both specifications are highly flexible to support different granularities of data to be encrypted and appropriate security levels. Based on the domain model, our code generator for Model-Driven Software Development generates code and configuration artifacts to control the encryption and decryption logic in the application and perform database schema modifications. Our encryption infrastructure outside the database (hence, application-level encryption) utilizes the security configuration to perform encryption and decryption.The generator relieves application developers from a significant amount of migration work required by application-level encryption. Hence, our approach combines the flexibility, security and independence from database vendors of application-level encryption and the transparency of database-level encryption. Our model-driven application-level encryption has been integrated into our eHealth Framework, a comprehensive platform for the development of electronic health care solutions. Our approach can be applied to other domains as well.

As users get accustomed to an increasing diverse range of mobile devices, personal computers, intelligent home and office appliances, as well as shared public devices, collaborative multi-device systems are emerging to benefit from using together devices of different users and different capabilities. This paper presents a conceptual framework for the systematic design of such systems in symbiotic environments. It distinguishes between information and interaction spaces. Focusing on using devices together, we introduce relationships between interaction spaces and annotatable mappings between the information space and the interaction spaces. Customizable annotations and the corresponding transformation of information support the development of collaborative systems for different scenarios. To demonstrate the validity of our framework, we introduce an architecture for multi-device web-browsing systems, and an annotation vocabulary which particularly focuses on symbiotic environments. The evaluation of our implemented prototype confirms the usefulness of multi-browsing, and the need for supporting both developers and end users to individually annotate web pages.

This paper presents our novel framework, which creates user interfaces (UIs) for a variety of devices by annotating and reusing an existing one originally designed for large devices. It distinguishes itself from previous work by the unique combination of reusing existing UIs, intuitive graphical support and adaptation-based approach. It is extensible by supporting UI developers to build and integrate their customized transformation strategies into our framework.

This paper presents our novel graphical single-authoring framework, which automatically creates customized user interfaces (UI) for a variety of devices by reusing an existing UI originally designed for large devices. It distinguishes itself from other authoring frameworks by its intuitive graphical support, UI reuse and its extensibility.