@article{tong_chen_williams_2022, title={Identification of Transcription Factors Regulating SARS-CoV-2 Tropism Factor Expression by Inferring Cell-Type-Specific Transcriptional Regulatory Networks in Human Lungs}, volume={14}, ISSN={["1999-4915"]}, DOI={10.3390/v14040837}, abstractNote={Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that caused the coronavirus disease 2019 (COVID-19) pandemic. Though previous studies have suggested that SARS-CoV-2 cellular tropism depends on the host-cell-expressed proteins, whether transcriptional regulation controls SARS-CoV-2 tropism factors in human lung cells remains unclear. In this study, we used computational approaches to identify transcription factors (TFs) regulating SARS-CoV-2 tropism for different types of lung cells. We constructed transcriptional regulatory networks (TRNs) controlling SARS-CoV-2 tropism factors for healthy donors and COVID-19 patients using lung single-cell RNA-sequencing (scRNA-seq) data. Through differential network analysis, we found that the altered regulatory role of TFs in the same cell types of healthy and SARS-CoV-2-infected networks may be partially responsible for differential tropism factor expression. In addition, we identified the TFs with high centralities from each cell type and proposed currently available drugs that target these TFs as potential candidates for the treatment of SARS-CoV-2 infection. Altogether, our work provides valuable cell-type-specific TRN models for understanding the transcriptional regulation and gene expression of SARS-CoV-2 tropism factors.}, number={4}, journal={VIRUSES-BASEL}, author={Tong, Haonan and Chen, Hao and Williams, Cranos M.}, year={2022}, month={Apr} }
 @misc{buckner_tong_ottley_williams_2021, title={High-throughput image segmentation and machine learning approaches in the plant sciences across multiple scales}, volume={5}, ISSN={["2397-8562"]}, url={https://doi.org/10.1042/ETLS20200273}, DOI={10.1042/ETLS20200273}, abstractNote={Agriculture has benefited greatly from the rise of big data and high-performance computing. The acquisition and analysis of data across biological scales have resulted in strategies modeling inter- actions between plant genotype and environment, models of root architecture that provide insight into resource utilization, and the elucidation of cell-to-cell communication mechanisms that are instrumental in plant development. Image segmentation and machine learning approaches for interpreting plant image data are among many of the computational methodologies that have evolved to address challenging agricultural and biological problems. These approaches have led to contributions such as the accelerated identification of gene that modulate stress responses in plants and automated high-throughput phenotyping for early detection of plant diseases. The continued acquisition of high throughput imaging across multiple biological scales provides opportunities to further push the boundaries of our understandings quicker than ever before. In this review, we explore the current state of the art methodologies in plant image segmentation and machine learning at the agricultural, organ, and cellular scales in plants. We show how the methodologies for segmentation and classification differ due to the diversity of physical characteristics found at these different scales. We also discuss the hardware technologies most commonly used at these different scales, the types of quantitative metrics that can be extracted from these images, and how the biological mechanisms by which plants respond to abiotic/biotic stresses or genotypic modifications can be extracted from these approaches.}, number={2}, journal={EMERGING TOPICS IN LIFE SCIENCES}, publisher={Portland Press Ltd.}, author={Buckner, Eli and Tong, Haonan and Ottley, Chanae and Williams, Cranos}, editor={Jez, Joseph M. and Topp, Christopher N.Editors}, year={2021}, pages={239–248} }