Learning COVID-19 network from literature databases using core decomposition
| dc.contributor.author | Guo, Yang | |
| dc.contributor.supervisor | Zhang, Xuekui | |
| dc.contributor.supervisor | Xing, Li | |
| dc.date.accessioned | 2021-07-22T19:46:03Z | |
| dc.date.available | 2021-07-22T19:46:03Z | |
| dc.date.copyright | 2021 | en_US |
| dc.date.issued | 2021-07-22 | |
| dc.degree.department | Department of Mathematics and Statistics | |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | The SARS-CoV-2 coronavirus is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein-protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database and the PPI network. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the PPI network in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs, and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The majority types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to known PPI network nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/13166 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | COVID-19 | en_US |
| dc.subject | Core Decomposition | en_US |
| dc.subject | Network Inference | en_US |
| dc.subject | Data Mining | en_US |
| dc.subject | Graph Theory | en_US |
| dc.title | Learning COVID-19 network from literature databases using core decomposition | en_US |
| dc.type | Thesis | en_US |