Data augmentation based semi-supervised method to improve COVID-19 CT classification.

The Coronavirus (COVID-19) outbreak of December 2019 has become a serious threat to people around the world, creating a health crisis that infected millions of lives, as well as destroying the global economy. Early detection and diagnosis are essential to prevent further transmission. The detection of COVID-19 computed tomography images is one of the important approaches to rapid diagnosis. Many different branches of deep learning methods have played an important role in this area, including transfer learning, contrastive learning, ensemble strategy, etc. However, these works require a large number of samples of expensive manual labels, so in order to save costs, scholars adopted semi-supervised learning that applies only a few labels to classify COVID-19 CT images. Nevertheless, the existing semi-supervised methods focus primarily on class imbalance and pseudo-label filtering rather than on pseudo-label generation. Accordingly, in this paper, we organized a semi-supervised classification framework based on data augmentation to classify the CT images of COVID-19. We revised the classic teacher-student framework and introduced the popular data augmentation method Mixup, which widened the distribution of high confidence to improve the accuracy of selected pseudo-labels and ultimately obtain a model with better performance. For the COVID-CT dataset, our method makes precision, F1 score, accuracy and specificity 21.04%, 12.95%, 17.13% and 38.29% higher than average values for other methods respectively, For the SARS-COV-2 dataset, these increases were 8.40%, 7.59%, 9.35% and 12.80% respectively. For the Harvard Dataverse dataset, growth was 17.64%, 18.89%, 19.81% and 20.20% respectively. The codes are available at https://github.com/YutingBai99/COVID-19-SSL.

Heterogeneous graph attention networks for drug virus association prediction.

Coronavirus Disease-19 (COVID-19) has lead global epidemics with high morbidity and mortality. However, there are currently no proven effective drugs targeting COVID-19. Identifying drug-virus associations can not only provide insights into the understanding of drug-virus interaction mechanism, but also guide and facilitate the screening of compound candidates for antiviral drug discovery. Since conventional experiment methods are time-consuming, laborious and expensive, computational methods to identify potential drug candidates for viruses (e.g., COVID-19) provide an alternative strategy. In this work, we propose a novel framework of Heterogeneous Graph Attention Networks for Drug-Virus Association predictions, named HGATDVA. First, we fully incorporate multiple sources of biomedical data, e.g., drug chemical information, virus genome sequences and viral protein sequences, to construct abundant features for drugs and viruses. Second, we construct two drug-virus heterogeneous graphs. For each graph, we design a self-enhanced graph attention network (SGAT) to explicitly model the dependency between a node and its local neighbors and derive the graph-specific representations for nodes. Third, we further develop a neural network architecture with tri-aggregator to aggregate the graph-specific representations to generate the final node representations. Extensive experiments were conducted on two datasets, i.e., DrugVirus and MDAD, and the results demonstrated that our model outperformed 7 state-of-the-art methods. Case study on SARS-CoV-2 validated the effectiveness of our model in identifying potential drugs for viruses.

Predicting human microbe-drug associations via graph convolutional network with conditional random field.

MOTIVATION: Human microbes play critical roles in drug development and precision medicine. How to systematically understand the complex interaction mechanism between human microbes and drugs remains a challenge nowadays. Identifying microbe-drug associations can not only provide great insights into understanding the mechanism, but also boost the development of drug discovery and repurposing. Considering the high cost and risk of biological experiments, the computational approach is an alternative choice. However, at present, few computational approaches have been developed to tackle this task. RESULTS: In this work, we leveraged rich biological information to construct a heterogeneous network for drugs and microbes, including a microbe similarity network, a drug similarity network and a microbe-drug interaction network. We then proposed a novel graph convolutional network (GCN)-based framework for predicting human Microbe-Drug Associations, named GCNMDA. In the hidden layer of GCN, we further exploited the Conditional Random Field (CRF), which can ensure that similar nodes (i.e. microbes or drugs) have similar representations. To more accurately aggregate representations of neighborhoods, an attention mechanism was designed in the CRF layer. Moreover, we performed a random walk with restart-based scheme on both drug and microbe similarity networks to learn valuable features for drugs and microbes, respectively. Experimental results on three different datasets showed that our GCNMDA model consistently achieved better performance than seven state-of-the-art methods. Case studies for three microbes including SARS-CoV-2 and two antimicrobial drugs (i.e. Ciprofloxacin and Moxifloxacin) further confirmed the effectiveness of GCNMDA in identifying potential microbe-drug associations. AVAILABILITY AND IMPLEMENTATION: Python codes and dataset are available at: https://github.com/longyahui/GCNMDA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Family exposure and the impact of containment measures to children with coronavirus disease 2019 outside Hubei, China: a cross-sectional study.

BACKGROUND: In response to the ongoing epidemic of coronavirus disease 2019 (COVID-19), China has carried out restrictive disease containment measures across the country. METHODS: In this cross-sectional study, we collected demographic and epidemiological data of 376 laboratory-confirmed cases of COVID-19 among children younger than 18 years of age. Using descriptive statistics and odds ratios, we described the odds of exposure outside the family after the implementation of control measures compared to before. RESULTS: Children diagnosed on or after February 4, 2020, had a lower odds of exposure to COVID-19 outside of the family compared to those diagnosed before February 3, 2020 (OR =0.594, 95% CI: 0.391 to 0.904). In the stratified analysis, children aged 0 to 5 years had the lowest odds of exposure outside of the family (OR =0.420, 95% CI: 0.196 to 0.904) compared to the other age groups assessed. CONCLUSIONS: Our study on the children infected with COVID-19 as well as their exposure within family provided evidence that the implementation of containment measures was effective in reducing the odds of exposure outside of the family, especially for preschool children. Continuation of these efforts, coupled with tailored prevention and health education messaging for younger aged children, may help to reduce the transmission of COVID-19 among children until other therapeutic interventions or vaccines are available.