Multimodal negative sentiment recognition of online public opinion on public health emergencies based on graph convolutional networks and ensemble learning

To improve the effect of multimodal negative sentiment recognition of online public opinion on public health emergencies, we constructed a novel multimodal fine-grained negative sentiment recognition model based on graph convolutional networks (GCN) and ensemble learning. This model comprises BERT and ViT-based multimodal feature representation, GCN-based feature fusion, multiple classifiers, and ensemble learning-based decision fusion. Firstly, the image-text data about COVID-19 is collected from Sina Weibo, and the text and image features are extracted through BERT and ViT, respectively. Secondly, the image-text fused features are generated through GCN in the constructed microblog graph. Finally, AdaBoost is trained to decide the final sentiments recognized by the best classifiers in image, text, and image-text fused features. The results show that the F1-score of this model is 84.13% in sentiment polarity recognition and 82.06% in fine-grained negative sentiment recognition, improved by 4.13% and 7.55% compared to the optimal recognition effect of image-text feature fusion, respectively. © 2023 Elsevier Ltd

The Graph Convolutional Networks Framework for Predicting Pandemic Impact on Stock Prices

Covid-19 has dealt an unprecedented hit to the global economy and all industries, with varying degrees of decline from retail to real estate. This volatility is most evident in stock prices. Previous stock price forecasting methods typically used historical data for each stock as a separate input into the system. This paper proposes an attention-based parallel graph convolutional network framework, which consists of two parallel GCNs. The first GCN takes stock features as input, and the second GCN takes other industry features as input, and sets an attention model to reflect the pairwise interactions between networks. Experimental results on selected stock data show that the model outperforms both the LSTM model and the GCN model in accuracy and F1 score. © 2022 IEEE.

Explainable Prediction of the Severity of COVID-19 Outbreak for US Counties

Ever since the COVID-19 outbreak, various works have focused on using multitude of different static and dynamic features to aid the prediction of disease forecasting models. However, in the absence of historical pandemic data these models will not be able to give any meaningful insight about the areas which are most likely to be affected based on preexisting conditions. Furthermore, the black box nature of neural networks often becomes an impediment for the concerned authorities to derive any meaning from. In this paper, we propose a novel explainable Graph Neural Network (GNN) framework called Graph-COVID-19-Explainer (GC-Explainer) that gives explainable prediction for the severity of the spread during initial outbreak. We utilize a comprehensive set of static population characteristics to use as node features of Graph where each node corresponds to a geographical region. Unlike post-hoc methods of GNN explanations, we propose a framework for learning important features during the training of the model. We further apply our model on real-world early pandemic data to show the validity of our approach. Through GC-Explainer, we show that static features along with spatial dependency among regions can be used to explain the varied degree of severity in outbreak during the early part of the pandemic and provide a framework to identify the at-risk areas for any infectious disease outbreak, especially when historical data is not available. © 2022 IEEE.

Recent computational drug repositioning strategies against SARS-CoV-2.

Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning.

Machine Learning - Based Diagnosis of Covid-19 using Clinical Data

Coronavirus (COVID-19) is a worldwide pandemic caused by SARS Coronavirus 2. (SARS-CoV-2). The COVID-19 epidemic has put global healthcare systems in jeopardy. This study's purpose is to develop and evaluate an automated COVID-19 infection detection system using machine learning and chest x-ray images. Early diagnosis and treatment may help avert major illness and even death. It is presently the most favoured and accurate approach for COVID-19 diagnosis. X-ray imaging of the chest may be used instead of the rRT-PCR test to look for early COVID-19 symptoms. A new machine learning (ML)-based analytical framework for automated COVID-19 diagnosis is created utilizing chest X-ray pictures of likely patients. The proposed framework for COVID-19 disease diagnosis using X-ray images has a 99 percent accuracy for Covid and a 92 percent accuracy for Non-covid in two-class categorization. The investigation suggests the COVID-19 detection framework is better. © 2022 IEEE.

A Structure-Based B-cell Epitope Prediction Model Through Combing Local and Global Features.

B-cell epitopes (BCEs) are a set of specific sites on the surface of an antigen that binds to an antibody produced by B-cell. The recognition of BCEs is a major challenge for drug design and vaccines development. Compared with experimental methods, computational approaches have strong potential for BCEs prediction at much lower cost. Moreover, most of the currently methods focus on using local information around target residue without taking the global information of the whole antigen sequence into consideration. We propose a novel deep leaning method through combing local features and global features for BCEs prediction. In our model, two parallel modules are built to extract local and global features from the antigen separately. For local features, we use Graph Convolutional Networks (GCNs) to capture information of spatial neighbors of a target residue. For global features, Attention-Based Bidirectional Long Short-Term Memory (Att-BLSTM) networks are applied to extract information from the whole antigen sequence. Then the local and global features are combined to predict BCEs. The experiments show that the proposed method achieves superior performance over the state-of-the-art BCEs prediction methods on benchmark datasets. Also, we compare the performance differences between data with or without global features. The experimental results show that global features play an important role in BCEs prediction. Our detailed case study on the BCEs prediction for SARS-Cov-2 receptor binding domain confirms that our method is effective for predicting and clustering true BCEs.

PoseMapGait: A model-based Gait Recognition Method with Pose Estimation Maps and Graph Convolutional Networks

Gait recognition is a particularly effective way to avoid the spread of COVID-19 while people are under surveillance. Because of its advantages of non-contact and long-distance identification. One category of gait recognition methods is appearance-based, which usually extracts human silhouettes as the initial input feature and achieves high recognition rates. However, the silhouette-based feature is easily affected by the view, clothing, bag, and other external variations. Another category is based on model-based, one popular model-based feature is extracted from human skeletons. The skeleton-based feature is robust to many variations because it is less sensitive to human shape. However, the performance of skeleton-based methods suffers from recognition accuracy loss due to limited input information. In this paper, instead of relying on coordinates from skeletons, we exploit that pose estimation maps, the byproduct of pose estimation. It not only preserves richer cues of the human body compared with the skeleton-based feature, but also keeps the advantage of being less sensitive to human shape compared with the silhouette-based feature. Specifically, the evolution of pose estimation maps is decomposed as one heatmaps evolution feature (extracted by gaitMap-CNN) and one pose evolution feature (extracted by gaitPose-GCN), which denote the invariant features of whole body structure and body pose joints for gait recognition, respectively. Our method is evaluated on two large datasets, CASIA-B and the CMU Motion of Body (MoBo) dataset. The proposed method achieves the new state-of-the-art performance compared with recent advanced model-based methods.

DeepGLSTM: Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinity

Development of new drugs is an expensive and time-consuming process. Due to the world-wide SARS-CoV-2 outbreak, it is essential that new drugs for SARS-CoV-2 are developed as soon as possible. Drug repurposing techniques can reduce the time span needed to develop new drugs by probing the list of existing FDA-approved drugs and their properties to reuse them for combating the new disease. We propose a novel architecture DeepGLSTM, which is a Graph Convolutional network and LSTM based method that predicts binding affinity values between the FDA-approved drugs and the viral proteins of SARS-CoV-2. Our proposed model has been trained on Davis, KIBA (Kinase Inhibitor Bioactivity), DTC (Drug Target Commons), Metz, ToxCast and STITCH datasets. We use our novel architecture to predict a Combined Score (calculated using Davis and KIBA score) of 2,304 FDA-approved drugs against 5 viral proteins. On the basis of the Combined Score, we prepare a list of the top-18 drugs with the highest binding affinity for 5 viral proteins present in SARS-CoV-2. Subsequently, this list may be used for the creation of new useful drugs. Copyright © 2022 by SIAM.

MDGNN: Microbial Drug Prediction Based on Heterogeneous Multi-Attention Graph Neural Network.

Human beings are now facing one of the largest public health crises in history with the outbreak of COVID-19. Traditional drug discovery could not keep peace with newly discovered infectious diseases. The prediction of drug-virus associations not only provides insights into the mechanism of drug-virus interactions, but also guides the screening of potential antiviral drugs. We develop a deep learning algorithm based on the graph convolutional networks (MDGNN) to predict potential antiviral drugs. MDGNN is consisted of new node-level attention and feature-level attention mechanism and shows its effectiveness compared with other comparative algorithms. MDGNN integrates the global information of the graph in the process of information aggregation by introducing the attention at node and feature level to graph convolution. Comparative experiments show that MDGNN achieves state-of-the-art performance with an area under the curve (AUC) of 0.9726 and an area under the PR curve (AUPR) of 0.9112. In this case study, two drugs related to SARS-CoV-2 were successfully predicted and verified by the relevant literature. The data and code are open source and can be accessed from https://github.com/Pijiangsheng/MDGNN.

Predicting the COVID-19 in the Metropolitan Region (Chile) using a GCN-LSTM neural network

COVID-19 is considered one of the largest pandemics in recent times. Predicting the number of future COVID-19 cases is extremely important for governments in order to make decisions about mobility restrictions, and for hospitals to be able to manage medical supplies, as well as health staff. Most of the predictions of COVID-19 cases are based on mathematical-epidemiological models such as the SEIR and SIR models. In our work, we propose a model of neural networks GCN-LSTM (Graph Convolutional Network - Long Short Term Memory) to predict the spatio-temporal rate incidence of COVID-19 in the Metropolitana Region, Chile. While the GCN network incorporates the spatial correlation in the nearby municipalities, the LSTM network considers the temporal correlation for the prediction over time. To interpolate the missing daily data for the network input, the use of the GAM (Generalized Additive Model) model is proposed. The results show better predictions for some municipalities with higher habitat density. © 2021 IEEE.

Interaction-Temporal GCN: A Hybrid Deep Framework For Covid-19 Pandemic Analysis.

The Covid-19 pandemic is still spreading around the world and seriously imperils humankind's health. This swift spread has caused the public to panic and look to scientists for answers. Fortunately, these scientists already have a wealth of data-the Covid-19 reports that each country releases, reports with valuable spatial-temporal properties. These data point toward some key actions that humans can take in their fight against Covid-19. Technically, the Covid-19 records can be described as sequences, which represent spatial-temporal linkages among the data elements with graph structure. Therefore, we propose a novel framework, the Interaction-Temporal Graph Convolution Network (IT-GCN), to analyze pandemic data. Specifically, IT-GCN introduces ARIMA into GCN to model the data which originate on nodes in a graph, indicating the severity of the pandemic in different cities. Instead of regular spatial topology, we construct the graph nodes with the vectors via ARIMA parameterization to find out the interaction topology underlying in the pandemic data. Experimental results show that IT-GCN is able to capture the comprehensive interaction-temporal topology and achieve well-performed short-term prediction of the Covid-19 daily infected cases in the United States. Our framework outperforms state-of-art baselines in terms of MAE, RMSE and MAPE. We believe that IT-GCN is a valid and reasonable method to forecast the Covid-19 daily infected cases and other related time-series. Moreover, the prediction can assist in improving containment policies.

The Molecular Mechanism of Multiple Organ Dysfunction and Targeted Intervention of COVID-19 Based on Time-Order Transcriptomic Analysis.

Coronavirus disease 2019 (COVID-19) pandemic is caused by the novel coronavirus that has spread rapidly around the world, leading to high mortality because of multiple organ dysfunction; however, its underlying molecular mechanism is unknown. To determine the molecular mechanism of multiple organ dysfunction, a bioinformatics analysis method based on a time-order gene co-expression network (TO-GCN) was performed. First, gene expression profiles were downloaded from the gene expression omnibus database (GSE161200), and a TO-GCN was constructed using the breadth-first search (BFS) algorithm to infer the pattern of changes in the different organs over time. Second, Gene Ontology enrichment analysis was used to analyze the main biological processes related to COVID-19. The initial gene modules for the immune response of different organs were defined as the research object. The STRING database was used to construct a protein-protein interaction network of immune genes in different organs. The PageRank algorithm was used to identify five hub genes in each organ. Finally, the Comparative Toxicogenomics Database played an important role in exploring the potential compounds that target the hub genes. The results showed that there were two types of biological processes: the body's stress response and cell-mediated immune response involving the lung, trachea, and olfactory bulb (olf) after being infected by COVID-19. However, a unique biological process related to the stress response is the regulation of neuronal signals in the brain. The stress response was heterogeneous among different organs. In the lung, the regulation of DNA morphology, angiogenesis, and mitochondrial-related energy metabolism are specific biological processes related to the stress response. In particular, an effect on tracheal stress response was made by the regulation of protein metabolism and rRNA metabolism-related biological processes, as biological processes. In the olf, the distinctive stress responses consist of neural signal transmission and brain behavior. In addition, myeloid leukocyte activation and myeloid leukocyte-mediated immunity in response to COVID-19 can lead to a cytokine storm. Immune genes such as SRC, RHOA, CD40LG, CSF1, TNFRSF1A, FCER1G, ICAM1, LAT, LCN2, PLAU, CXCL10, ICAM1, CD40, IRF7, and B2M were predicted to be the hub genes in the cytokine storm. Furthermore, we inferred that resveratrol, acetaminophen, dexamethasone, estradiol, statins, curcumin, and other compounds are potential target drugs in the treatment of COVID-19.

Amino acid sensing pathway: A major check point in the pathogenesis of obesity and COVID-19.

Obesity and obesogenic comorbidities have been associated with COVID-19 susceptibility and mortality. However, the mechanism of such correlations requires an in-depth understanding. Overnutrition/excess serum amino acid profile during obesity has been linked with inflammation and reprogramming of translational machinery through hyperactivation of amino acid sensor mammalian target of rapamycin (mTOR), which is exploited by SARS-CoV-2 for its replication. Conversely, we have shown that the activation of general control nonderepressible 2 (GCN2)-dependent amino acid starvation sensing pathway suppresses intestinal inflammation by inhibiting the production of reactive oxygen species (ROS) and interleukin-1 beta (IL-1ß). While activation of GCN2 has shown to mitigate susceptibility to dengue infection, GCN2 deficiency increases viremia and inflammation-associated pathologies. These findings reveal that the amino acid sensing pathway plays a significant role in controlling inflammation and viral infections. The current fact is that obesity/excess amino acids/mTOR activation aggravates COVID-19, and it might be possible that activation of amino acid starvation sensor GCN2 has an opposite effect. This article focuses on the amino acid sensing pathways through which host cells sense the availability of amino acids and reprogram the host translation machinery to mount an effective antiviral response. Besides, how SARS-CoV-2 hijack and exploit amino acid sensing pathway for its replication and pathogenesis is also discussed.