Drug Potency Prediction of SARS-CoV-2 Main Protease Inhibitors Based on a Graph Generative Model.

The prediction of a ligand potency to inhibit SARS-CoV-2 main protease (M-pro) would be a highly helpful addition to a virtual screening process. The most potent compounds might then be the focus of further efforts to experimentally validate their potency and improve them. A computational method to predict drug potency, which is based on three main steps, is defined: (1) defining the drug and protein in only one 3D structure; (2) applying graph autoencoder techniques with the aim of generating a latent vector; and (3) using a classical fitting model to the latent vector to predict the potency of the drug. Experiments in a database of 160 drug-M-pro pairs, from which the pIC50 is known, show the ability of our method to predict their drug potency with high accuracy. Moreover, the time spent to compute the pIC50 of the whole database is only some seconds, using a current personal computer. Thus, it can be concluded that a computational tool that predicts, with high reliability, the pIC50 in a cheap and fast way is achieved. This tool, which can be used to prioritize which virtual screening hits, will be further examined in vitro.

A Web Knowledge-Driven Multimodal Retrieval Method in Computational Social Systems: Unsupervised and Robust Graph Convolutional Hashing

Multimodal retrieval has received widespread consideration since it can commendably provide massive related data support for the development of computational social systems (CSSs). However, the existing works still face the following challenges: 1) rely on the tedious manual marking process when extended to CSS, which not only introduces subjective errors but also consumes abundant time and labor costs;2) only using strongly aligned data for training, lacks concern for the adjacency information, which makes the poor robustness and semantic heterogeneity gap difficult to be effectively fit;and 3) mapping features into real-valued forms, which leads to the characteristics of high storage and low retrieval efficiency. To address these issues in turn, we have designed a multimodal retrieval framework based on web-knowledge-driven, called unsupervised and robust graph convolutional hashing (URGCH). The specific implementations are as follows: first, a "secondary semantic self-fusion" approach is proposed, which mainly extracts semantic-rich features through pretrained neural networks, constructs the joint semantic matrix through semantic fusion, and eliminates the process of manual marking;second, a "adaptive computing" approach is designed to construct enhanced semantic graph features through the knowledge-infused of neighborhoods and uses graph convolutional networks for knowledge fusion coding, which enables URGCH to sufficiently fit the semantic modality gap while obtaining satisfactory robustness features;Third, combined with hash learning, the multimodality data are mapped into the form of binary code, which reduces storage requirements and improves retrieval efficiency. Eventually, we perform plentiful experiments on the web dataset. The results evidence that URGCH exceeds other baselines about 1%-3.7% in mean average precisions (MAPs), displays superior performance in all the aspects, and can meaningfully provide multimodal data retrieval services to CSS.

Forecasting the COVID-19 Space-time Dynamics in Brazil with Convolutional Graph Neural Networks and Transport Modals

One of the major challenges imposed by the SARS-CoV-2 pandemic is the lack of pattern in which the virus spreads, making it difficult to create effective policies to prevent and tackle the pandemic. Several approaches have been proposed to understand the virus behavior and anticipate its infection and death curves at country ans state levels, thus supporting containment measures. Those initiatives generalize well for general extents and decisions, but they do not predict so well the trajectory of the virus through specific regions, such as municipalities, considering their distinct interconnection profiles. Specially in countries with continental dimensions, like Brazil, too general decisions imply that containment measures are applied either too soon or too late. This study presents a novel scalable alternative to forecast the numbers of case and death by SARS-CoV-2, according to the influence that certain regions exert on others. By exploiting a single-model architecture of graph convolutional networks with recurrent networks, our approach maps the main access routes to municipalities in Brazil using the modals of transport, and processes this information via neural network algorithms to forecast at the municipal level ans for the whole country. We compared the performance in forecasting the pandemic daily numbers with three baseline models using Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (sMAPE) and Normalized Root Mean Square Error (NRMSE) metrics, with the forecasting horizon varying from 1 to 25 days. Results show that the proposed model overcomes the baselines when considering the MAE and NRMSE (p ˂0.01), being specially suitable for forecasts from 14 to 24 days ahead. Author

Detecting Vaccine Skepticism on Twitter Using Heterogeneous Information Networks

Identifying social media users who are skeptical of the COVID-19 vaccine is an important step in understanding and refuting negative stance taking on vaccines. While previous work on Twitter data places individual messages or whole communities as their focus, this paper aims to detect stance at the user level. We develop a system that classifies Dutch Twitter users, incorporating not only the texts that users produce, but also their actions in the form of following and retweeting. These heterogeneous data are modelled in a graph structure. Graph Convolutional Networks are trained to learn whether user nodes belong to the skeptical or non-skeptical group. Results show that all types of information are used by the model, and that especially user biographies, follows and retweets improve the predictions. On a test set of unseen users, performance declines somewhat, which is expected considering these users tweeted less and had fewer connections in the graph on average. To consider multiple degrees of vaccine skepticism, the test set was annotated with more fine-grained labels and the model was repurposed to do multiclass classification. While the model trained on binary labels was unsuited for this additional task, heterogeneous information networks were found useful to both accurately model and visualize complex user behaviors. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Mental Health Management of English Teachers in English Teaching Under the COVID-19 Era.

Background: The COVID-19 pandemic has brought new challenges and attention to the mental health of all social groups, making mental health increasingly necessary and important. However, people only focus on the mental health of undergraduates, and the mental health of teachers has not received much attention from society. College teachers are the backbone of the teachers' group, and their mental health not only affects the teaching quality and research level but also plays an important role in the mental health and personality development of undergraduates. Method: During the COVID-19 pandemic, online teaching is a major challenge for college teachers, especially English teachers. To this end, this article proposes a bipartite graph convolutional network (BGCN) model based on the psychological test questionnaire and its structural characteristics for the recognition of the mental health crisis. Results: Experimental results show that the proposed BGCN model is superior to neural network algorithms and other machine learning algorithms in accuracy, precision, F1, and recall and can be well used for the mental health management of English teachers in the era of COVID-19.

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.

Learning Discriminant Spatial Features with Deep Graph-based Convolutions for Occluded Face Detection

The use of face masks has become a widespread non-pharmaceutical practice to mitigate the transmission of COVID-19. However, achieving accurate facial detection while people wear masks or similar face occlusions is a major challenge. This paper introduces a model to detect occluded or masked faces based on fused convolutional graphs. This model includes a deep neural architecture with two spatial-based graphs that rely on a set of key facial features. First, a distance graph is used to identify geographical similarity between the facial nodes that represent certain key face parts. Second, a correlation graph is formulated to compute the correlations between every two nodes that represent two different augmented facial modalities. Transfer learning is then performed using a pretrained deep architecture as a baseline to map the semantic information into multiple feature filters. Then, discriminant graph convolutions are constructed based on the fusion of distance and correlation graphs. This model evaluates two tasks of facial detection, which are the binary detection of masked or unmasked faces, and multi-category detection of masked, unmasked, or occluded face with no mask. The experimental results on two benchmarking real-world datasets show that the proposed deep learning model is highly effective with an accuracy of 98% achieved in binary detection. Even with high variance in image occlusions, our proposed model has great promise in detecting and distinguishing between types of facial occlusion with an accuracy of 86% reported in multi-category detection. Author

Recurrent Multi-task Graph Convolutional Networks for COVID-19 Knowledge Graph Link Prediction

Knowledge graphs (KGs) are a way to model data involving intricate relations between a number of entities. Understanding the information contained in KGs and predicting what hidden relations may be present can provide valuable domain-specific knowledge. Thus, we use data provided by the 5th Annual Oak Ridge National Laboratory Smoky Mountains Computational Sciences Data Challenge 2 as well as auxiliary textual data processed with natural language processing techniques to form and analyze a COVID-19 KG of biomedical concepts and research papers. Moreover, we propose a recurrent graph convolutional network model that predicts both the existence of novel links between concepts in this COVID-19 KG and the time at which the link will form. We demonstrate our model’s promising performance against several baseline models. The utilization of our work can give insights that are useful in COVID-19-related fields such as drug development and public health. All code for our paper is publicly available at https://github.com/RemingtonKim/SMCDC2021. © 2022, Springer Nature Switzerland AG.

A Survey on the Use of Graph Convolutional Networks for Combating Fake News

The combat against fake news and disinformation is an ongoing, multi-faceted task for researchers in social media and social networks domains, which comprises not only the detection of false facts in published content but also the detection of accountability mechanisms that keep a record of the trustfulness of sources that generate news and, lately, of the networks that deliberately distribute fake information. In the direction of detecting and handling organized disinformation networks, major social media and social networking sites are currently developing strategies and mechanisms to block such attempts. The role of machine learning techniques, especially neural networks, is crucial in this task. The current work focuses on the popular and promising graph representation techniques and performs a survey of the works that employ Graph Convolutional Networks (GCNs) to the task of detecting fake news, fake accounts and rumors that spread in social networks. It also highlights the available benchmark datasets employed in current research for validating the performance of the proposed methods. This work is a comprehensive survey of the use of GCNs in the combat against fake news and aims to be an ideal starting point for future researchers in the field.