This page has only limited features, please log in for full access.
Antibody therapeutics and vaccines are among our last resort to end the raging COVID-19 pandemic.
Jiahui Chen; Kaifu Gao; Rui Wang; Guo-Wei Wei. Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies. Chemical Science 2021, 12, 6929 -6948.
AMA StyleJiahui Chen, Kaifu Gao, Rui Wang, Guo-Wei Wei. Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies. Chemical Science. 2021; 12 (20):6929-6948.
Chicago/Turabian StyleJiahui Chen; Kaifu Gao; Rui Wang; Guo-Wei Wei. 2021. "Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies." Chemical Science 12, no. 20: 6929-6948.
Toxicity analysis is a major challenge in drug design and discovery. Recently significant progress has been made through machine learning due to its accuracy, efficiency, and lower cost. US Toxicology in the 21st Century (Tox21) screened a large library of compounds, including approximately 12 000 environmental chemicals and drugs, for different mechanisms responsible for eliciting toxic effects. The Tox21 Data Challenge offered a platform to evaluate different computational methods for toxicity predictions. Inspired by the success of multiscale weighted colored graph (MWCG) theory in protein–ligand binding affinity predictions, we consider MWCG theory for toxicity analysis. In the present work, we develop a geometric graph learning toxicity (GGL-Tox) model by integrating MWCG features and the gradient boosting decision tree (GBDT) algorithm. The benchmark tests of the Tox21 Data Challenge are employed to demonstrate the utility and usefulness of the proposed GGL-Tox model. An extensive comparison with other state-of-the-art models indicates that GGL-Tox is an accurate and efficient model for toxicity analysis and prediction.
Jian Jiang; Rui Wang; Guo-Wei Wei. GGL-Tox: Geometric Graph Learning for Toxicity Prediction. Journal of Chemical Information and Modeling 2021, 61, 1691 -1700.
AMA StyleJian Jiang, Rui Wang, Guo-Wei Wei. GGL-Tox: Geometric Graph Learning for Toxicity Prediction. Journal of Chemical Information and Modeling. 2021; 61 (4):1691-1700.
Chicago/Turabian StyleJian Jiang; Rui Wang; Guo-Wei Wei. 2021. "GGL-Tox: Geometric Graph Learning for Toxicity Prediction." Journal of Chemical Information and Modeling 61, no. 4: 1691-1700.
Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a worldwide devastating effect. Understanding the evolution and transmission of SARS-CoV-2 is of paramount importance for controlling, combating and preventing COVID-19. Due to the rapid growth in both the number of SARS-CoV-2 genome sequences and the number of unique mutations, the phylogenetic analysis of SARS-CoV-2 genome isolates faces an emergent large-data challenge. We introduce a dimension-reduced K-means clustering strategy to tackle this challenge. We examine the performance and effectiveness of three dimension-reduction algorithms: principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP). By using four benchmark datasets, we found that UMAP is the best-suited technique due to its stable, reliable, and efficient performance, its ability to improve clustering accuracy, especially for large Jaccard distanced-based datasets, and its superior clustering visualization. The UMAP-assisted K-means clustering enables us to shed light on increasingly large datasets from SARS-CoV-2 genome isolates.
Yuta Hozumi; Rui Wang; Changchuan Yin; Guo-Wei Wei. UMAP-assisted K-means clustering of large-scale SARS-CoV-2 mutation datasets. Computers in Biology and Medicine 2021, 131, 104264 -104264.
AMA StyleYuta Hozumi, Rui Wang, Changchuan Yin, Guo-Wei Wei. UMAP-assisted K-means clustering of large-scale SARS-CoV-2 mutation datasets. Computers in Biology and Medicine. 2021; 131 ():104264-104264.
Chicago/Turabian StyleYuta Hozumi; Rui Wang; Changchuan Yin; Guo-Wei Wei. 2021. "UMAP-assisted K-means clustering of large-scale SARS-CoV-2 mutation datasets." Computers in Biology and Medicine 131, no. : 104264-104264.
Jiahui Chen; Rui Wang; Guo-Wei Wei. SARS-CoV-2 becoming more infectious as revealed by algebraic topology and deep learning. Communications in Information and Systems 2021, 21, 31 -36.
AMA StyleJiahui Chen, Rui Wang, Guo-Wei Wei. SARS-CoV-2 becoming more infectious as revealed by algebraic topology and deep learning. Communications in Information and Systems. 2021; 21 (1):31-36.
Chicago/Turabian StyleJiahui Chen; Rui Wang; Guo-Wei Wei. 2021. "SARS-CoV-2 becoming more infectious as revealed by algebraic topology and deep learning." Communications in Information and Systems 21, no. 1: 31-36.
One of the major challenges in controlling the coronavirus disease 2019 (COVID-19) outbreak is its asymptomatic transmission. The pathogenicity and virulence of asymptomatic COVID-19 remain mysterious. On the basis of the genotyping of 75775 SARS-CoV-2 genome isolates, we reveal that asymptomatic infection is linked to SARS-CoV-2 11083G>T mutation (i.e., L37F at nonstructure protein 6 (NSP6)). By analyzing the distribution of 11083G>T in various countries, we unveil that 11083G>T may correlate with the hypotoxicity of SARS-CoV-2. Moreover, we show a global decaying tendency of the 11083G>T mutation ratio indicating that 11083G>T hinders the SARS-CoV-2 transmission capacity. Artificial intelligence, sequence alignment, and network analysis are applied to show that NSP6 mutation L37F may have compromised the virus’s ability to undermine the innate cellular defense against viral infection via autophagy regulation. This assessment is in good agreement with our genotyping of the SARS-CoV-2 evolution and transmission across various countries and regions over the past few months.
Rui Wang; Jiahui Chen; Yuta Hozumi; Changchuan Yin; Guo-Wei Wei. Decoding Asymptomatic COVID-19 Infection and Transmission. The Journal of Physical Chemistry Letters 2020, 11, 10007 -10015.
AMA StyleRui Wang, Jiahui Chen, Yuta Hozumi, Changchuan Yin, Guo-Wei Wei. Decoding Asymptomatic COVID-19 Infection and Transmission. The Journal of Physical Chemistry Letters. 2020; 11 (23):10007-10015.
Chicago/Turabian StyleRui Wang; Jiahui Chen; Yuta Hozumi; Changchuan Yin; Guo-Wei Wei. 2020. "Decoding Asymptomatic COVID-19 Infection and Transmission." The Journal of Physical Chemistry Letters 11, no. 23: 10007-10015.
The transmission and evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are of paramount importance in controlling and combating the coronavirus disease 2019 (COVID-19) pandemic. Currently, over 15,000 SARS-CoV-2 single mutations have been recorded, which have a great impact on the development of diagnostics, vaccines, antibody therapies, and drugs. However, little is known about SARS-CoV-2’s evolutionary characteristics and general trend. In this work, we present a comprehensive genotyping analysis of existing SARS-CoV-2 mutations. We reveal that host immune response via APOBEC and ADAR gene editing gives rise to near 65% of recorded mutations. Additionally, we show that children under age five and the elderly may be at high risk from COVID-19 because of their overreaction to the viral infection. Moreover, we uncover that populations of Oceania and Africa react significantly more intensively to SARS-CoV-2 infection than those of Europe and Asia, which may explain why African Americans were shown to be at increased risk of dying from COVID-19, in addition to their high risk of COVID-19 infection caused by systemic health and social inequities. Finally, our study indicates that for two viral genome sequences of the same origin, their evolution order may be determined from the ratio of mutation type, C > T over T > C.
Rui Wang; Yuta Hozumi; Yong-Hui Zheng; Changchuan Yin; Guo-Wei Wei. Host Immune Response Driving SARS-CoV-2 Evolution. Viruses 2020, 12, 1095 .
AMA StyleRui Wang, Yuta Hozumi, Yong-Hui Zheng, Changchuan Yin, Guo-Wei Wei. Host Immune Response Driving SARS-CoV-2 Evolution. Viruses. 2020; 12 (10):1095.
Chicago/Turabian StyleRui Wang; Yuta Hozumi; Yong-Hui Zheng; Changchuan Yin; Guo-Wei Wei. 2020. "Host Immune Response Driving SARS-CoV-2 Evolution." Viruses 12, no. 10: 1095.