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1.
Journal of Image and Graphics ; 27(3):750-761, 2022.
Article in Chinese | Scopus | ID: covidwho-1789677

ABSTRACT

Objective: The primary routine clinical diagnosis of COVID-19(corona virus disease 2019) is usually conducted based on epidemiological history, clinical manifestations and various laboratory detection methods, including nucleic acid amplification test (NAAT), computed tomography (CT) scan and serological techniques. However, manual detection is costly, time-consuming and leads to the potential increase of the infection risk of clinicians. As a good alternative, artificial intelligence techniques on available data from laboratory tests play an important role in the confirmation of COVID-19 cases. Some studies have been designed for distinguishing between novel coronavirus pneumonia, community-acquired pneumonia and normal people by graph neural network. However, these studies leverage the relationships between features to build a topological structure graph (e.g., connecting the nodes with high similarity), while ignoring the inner relationships between different parts of the lung, and thus limiting the performance of their models. To address this issue, we propose a graph neural network with hierarchical information inherent to the physical structure of lungs for the improved diagnosis of COVID-19. Besides, an attention mechanism is introduced to capture the discriminative features of different severities of infection in the left and right lobes of different patients. Method: Firstly, the topological structure is constructed based on the lungs' physical structure, and different lung segments are regarded as different nodes. Each node in the graph contains three kinds of handcrafted features, such as volume, density and mass feature, which reflect the infection in each lung segment and can be extracted from chest CT images using VB-Net. Secondly, based on graph neural network (GNN) and attention mechanism, we propose a novel structural attention graph neural network (SAGNN), which can perform the graph classification task. The SAGNN first aggregates the features in a given sample graph, and then uses the attention mechanism to effectively fuse the different features to obtain the final graph representation. This representation is then fed into a linear layer with softmax activation function that performs graph classification, so that the corresponding sample graph can be finally classified as a mild case or a severe one. To alleviate the effect of category imbalance on the classification results, we use the focal loss function. We optimize the proposed model via back propagation and learn the representations of graphs. Result: To verify the effectiveness of the proposed method, we compared SAGNN with several classical machine learning methods and graph classification methods on a real COVID-19 dataset, which includes 358 severe cases and 1 329 mild cases, provided by Shanghai Public Health Clinical Center. The result of comparative experiments was measured using three evaluation metrics including the sensitivity (SEN), the specificity (SPE) and the area under the receiver operating characteristic(ROC) curve (AUC). In the experiments, our model had a good performance, indicating the effectiveness of our model. Based on comparison with the classical machine learning methods and the graph neural network methods, SAGNN outperformed by 14.2%42.0% and 3.6%4.8% in terms of SEN, respectively. In terms of AUC, the performance of SAGNN increased by 8.9%18.7% and 3.1%3.6%, respectively. In addition, through the ablation experiments of SAGNN, we found that the SAGNN with attention mechanism outperformed by 2.4%, 1.4% and 1.1% in SPE, SEN and AUC than the SAGNN not with attention mechanism, respectively. The SAGNN with focal loss function outperformed by 2.1%, 1.1% and 0.9% in SPE, SEN and AUC than the SAGNN with cross-entropy loss function, respectively. Conclusion: In this work, we propose SAGNN, a new architecture for the diagnosis of severe and mild cases of COVID-19. Experimental results show the superior performance of SAGNN on classification task. Experimental results show that concatenating features of lung segments by t eir structure is effective. Moreover, we introduce an attention mechanism to distinguish the infection degree of right and left lungs. The focal loss is used to solve the issue of imbalanced group distribution, which further improves the overall network performance. We thus demonstrate the potential of SAGNN as clinical diagnosis support in this highly critical domain of medical intervention. We believe that our architecture provides a valuable case study for the early diagnosis of COVID-19, which is helpful for improvement in the field of computer-aided diagnosis and clinical practice. © 2022, Editorial Office of Journal of Image and Graphics. All right reserved.

2.
2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; : 556-561, 2021.
Article in English | Scopus | ID: covidwho-1722878

ABSTRACT

Clinical omics, especially gene expression data, have been widely studied and successfully applied for disease diagnosis using machine learning techniques. As genes often work interactively rather than individually, investigating co-functional gene modules can improve our understanding of disease mechanisms and facilitate disease state prediction. To this end, we in this paper propose a novel Multi-Level Enhanced Graph ATtention (MLE-GAT) network to explore the gene modules and intergene relational information contained in the omics data. In specific, we first format the omics data of each patient into co-expression graphs using weighted correlation network analysis (WGCNA) and then feed them to a well-designed multi-level graph feature fully fusion (MGFFF) module for disease diagnosis. For model interpretation, we develop a novel full-gradient graph saliency (FGS) mechanism to identify the disease-relevant genes. Comprehensive experiments show that our proposed MLE-GAT achieves state-of-the-art performance on transcriptomics data from TCGA-LGG/TCGA-GBM and proteomics data from COVID-19/non-COVID-19 patient sera. © 2021 IEEE.

3.
2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; : 779-784, 2021.
Article in English | Scopus | ID: covidwho-1722863

ABSTRACT

With the current raging spread of the COVID19, early forecasting of the future epidemic trend is of great significance to public health security. The COVID-19 is virulent and spreads widely. An outbreak in one region often triggers the spread of others, and regions with relatively close association would show a strong correlation in the spread of the epidemic. In the real world, many factors affect the spread of the outbreak between regions. These factors exist in the form of multimodal data, such as the time-series data of the epidemic, the geographic relationship, and the strength of social contacts between regions. However, most of the current work only uses historical epidemic data or single-modal geographic location data to forecast the spread of the epidemic, ignoring the correlation and complementarity in multi-modal data and its impact on the disease spread between regions. In this paper, we propose a Multimodal InformatioN fusion COVID-19 Epidemic forecasting model (MINE). It fuses inter-regional and intra-regional multi-modal information to capture the temporal and spatial relevance of the COVID-19 spread in different regions. Extensive experimental results show that the proposed method achieves the best results compared to state-of-art methods on benchmark datasets. © 2021 IEEE.

4.
Methods ; 198: 3-10, 2022 02.
Article in English | MEDLINE | ID: covidwho-1721113

ABSTRACT

The coronavirus disease 2019 (COVID-19) has outbreak since early December 2019, and COVID-19 has caused over 100 million cases and 2 million deaths around the world. After one year of the COVID-19 outbreak, there is no certain and approve medicine against it. Drug repositioning has become one line of scientific research that is being pursued to develop an effective drug. However, due to the lack of COVID-19 data, there is still no specific drug repositioning targeting the COVID-19. In this paper, we propose a framework for COVID-19 drug repositioning. This framework has several advantages that can be exploited: one is that a local graph aggregating representation is used across a heterogeneous network to address the data sparsity problem; another is the multi-hop neighbors of the heterogeneous graph are aggregated to recall as many COVID-19 potential drugs as possible. Our experimental results show that our COVDR framework performs significantly better than baseline methods, and the docking simulation verifies that our three potential drugs have the ability to against COVID-19 disease.


Subject(s)
COVID-19 , Pharmaceutical Preparations , Antiviral Agents , Drug Repositioning , Humans , Molecular Docking Simulation , SARS-CoV-2
5.
Math Biosci Eng ; 19(4): 3269-3284, 2022 01 21.
Article in English | MEDLINE | ID: covidwho-1667425

ABSTRACT

Research on the relationship between drugs and targets is the key to precision medicine. Ion channel is a kind of important drug targets. Aiming at the urgent needs of corona virus disease 2019 (COVID-19) treatment and drug development, this paper designed a mixed graph network model to predict the affinity between ion channel targets of COVID-19 and drugs. According to the simplified molecular input line entry specification (SMILES) code of drugs, firstly, the atomic features were extracted to construct the point sets, and edge sets were constructed according to atomic bonds. Then the undirected graph with atomic features was generated by RDKit tool and the graph attention layer was used to extract the drug feature information. Five ion channel target proteins were screened from the whole SARS-CoV-2 genome sequences of NCBI database, and the protein features were extracted by convolution neural network (CNN). Using attention mechanism and graph convolutional network (GCN), the extracted drug features and target features information were connected. After two full connection layers operation, the drug-target affinity was output, and model was obtained. Kiba dataset was used to train the model and determine the model parameters. Compared with DeepDTA, WideDTA, graph attention network (GAT), GCN and graph isomorphism network (GIN) models, it was proved that the mean square error (MSE) of the proposed model was decreased by 0.055, 0.04, 0.001, 0.046, 0.013 and the consistency index (CI) was increased by 0.028, 0.016, 0.003, 0.03 and 0.01, respectively. It can predict the drug-target affinity more accurately. According to the prediction results of drug-target affinity of SARS-CoV-2 ion channel targets, seven kinds of small molecule drugs acting on five ion channel targets were obtained, namely SCH-47112, Dehydroaltenusin, alternariol 5-o-sulfate, LPA1 antagonist 1, alternariol, butin, and AT-9283.These drugs provide a reference for drug repositioning and precise treatment of COVID-19.


Subject(s)
COVID-19 , Drug Repositioning , COVID-19/drug therapy , Humans , Ion Channels , Neural Networks, Computer , SARS-CoV-2
6.
Biology (Basel) ; 11(1)2021 Dec 27.
Article in English | MEDLINE | ID: covidwho-1581041

ABSTRACT

Accurate and timely diagnosis of COVID-19 is indispensable to control its spread. This study proposes a novel explainable COVID-19 diagnosis system called CGENet based on graph embedding and an extreme learning machine for chest CT images. We put forward an optimal backbone selection algorithm to select the best backbone for the CGENet based on transfer learning. Then, we introduced graph theory into the ResNet-18 based on the k-nearest neighbors. Finally, an extreme learning machine was trained as the classifier of the CGENet. The proposed CGENet was evaluated on a large publicly-available COVID-19 dataset and produced an average accuracy of 97.78% based on 5-fold cross-validation. In addition, we utilized the Grad-CAM maps to present a visual explanation of the CGENet based on COVID-19 samples. In all, the proposed CGENet can be an effective and efficient tool to assist COVID-19 diagnosis.

7.
Netw Model Anal Health Inform Bioinform ; 11(1): 6, 2022.
Article in English | MEDLINE | ID: covidwho-1588689

ABSTRACT

The transmittable spread of viral coronavirus (SARS-CoV-2) has resulted in a significant rise in global mortality. Due to lack of effective treatment, our aim is to generate a highly potent active molecule that can bind with the protein structure of SARS-CoV-2. Different machine learning and deep learning approaches have been proposed for molecule generation; however, most of these approaches represent the drug molecule and protein structure in 1D sequence, ignoring the fact that molecules are by nature in 3D structure, and because of this many critical properties are lost. In this work, a framework is proposed that takes account of both tertiary and sequential representations of molecules and proteins using Gated Graph Neural Network (GGNN), Knowledge graph, and Early Fusion approach. The generated molecules from GGNN are screened using Knowledge Graph to reduce the search space by discarding the non-binding molecules before being fed into the Early Fusion model. Further, the binding affinity score of the generated molecule is predicted using the early fusion approach. Experimental result shows that our framework generates valid and unique molecules with high accuracy while preserving the chemical properties. The use of a knowledge graph claims that the entire generated dataset of molecules was reduced by roughly 96% while retaining more than 85% of good binding desirable molecules and the rejection of more than 99% of fruitless molecules. Additionally, the framework was tested with two of the SARS-CoV-2 viral proteins: RNA-dependent-RNA polymerase (RdRp) and 3C-like protease (3CLpro).

8.
J Biomol Struct Dyn ; : 1-13, 2021 Dec 08.
Article in English | MEDLINE | ID: covidwho-1557011

ABSTRACT

COVID-19 is a worldwide health crisis seriously endangering the arsenal of antiviral and antibiotic drugs. It is urgent to find an effective antiviral drug against pandemic caused by the severe acute respiratory syndrome (Sars-Cov-2), which increases global health concerns. As it can be expensive and time-consuming to develop specific antiviral drugs, reuse of FDA-approved drugs that provide an opportunity to rapidly distribute effective therapeutics can allow to provide treatments with known preclinical, pharmacokinetic, pharmacodynamic and toxicity profiles that can quickly enter in clinical trials. In this study, using the structural information of molecules and proteins, a list of repurposed drug candidates was prepared again with the graph neural network-based GEFA model. The data set from the public databases DrugBank and PubChem were used for analysis. Using the Tanimoto/jaccard similarity analysis, a list of similar drugs was prepared by comparing the drugs used in the treatment of COVID-19 with the drugs used in the treatment of other diseases. The resultant drugs were compared with the drugs used in lung cancer and repurposed drugs were obtained again by calculating the binding strength between a drug and a target. The kinase inhibitors (erlotinib, lapatinib, vandetanib, pazopanib, cediranib, dasatinib, linifanib and tozasertib) obtained from the study can be used as an alternative for the treatment of COVID-19, as a combination of blocking agents (gefitinib, osimertinib, fedratinib, baricitinib, imatinib, sunitinib and ponatinib) such as ABL2, ABL1, EGFR, AAK1, FLT3 and JAK1, or antiviral therapies (ribavirin, ritonavir-lopinavir and remdesivir).Communicated by Ramaswamy H. Sarma.

9.
Neurocomputing ; 452: 592-605, 2021 Sep 10.
Article in English | MEDLINE | ID: covidwho-1002933

ABSTRACT

The widely spreading COVID-19 has caused thousands of hundreds of mortalities over the world in the past few months. Early diagnosis of the virus is of great significance for both of infected patients and doctors providing treatments. Chest Computerized tomography (CT) screening is one of the most straightforward techniques to detect pneumonia which was caused by the virus and thus to make the diagnosis. To facilitate the process of diagnosing COVID-19, we therefore developed a graph convolutional neural network ResGNet-C under ResGNet framework to automatically classify lung CT images into normal and confirmed pneumonia caused by COVID-19. In ResGNet-C, two by-products named NNet-C, ResNet101-C that showed high performance on detection of COVID-19 are simultaneously generated as well. Our best model ResGNet-C achieved an averaged accuracy at 0.9662 with an averaged sensitivity at 0.9733 and an averaged specificity at 0.9591 using five cross-validations on the dataset, which is comprised of 296 CT images. To our best knowledge, this is the first attempt at integrating graph knowledge into the COVID-19 classification task. Graphs are constructed according to the Euclidean distance between features extracted by our proposed ResNet101-C and then are encoded with the features to give the prediction results of CT images. Besides the high-performance system, which surpassed all state-of-the-art methods, our proposed graph construction method is simple, transferrable yet quite helpful for improving the performance of classifiers, as can be justified by the experimental results.

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