Integrative COVID-19 Biological Network Inference with Probabilistic Core Decomposition

• Activity Number: 55 - Statistical methods for data from single cell technologies
• Type: Contributed
• Date/Time: Sunday, August 8, 2021 : 3:30 PM to 5:20 PM
• Sponsor: WNAR
• Author(s): Yang Guo* and Fatemeh Esfahani and Xiaojian Shao and Venkatesh Srinivasan and Alex Thomo and Li Xing and Xuekui Zhang
• Companies: University of Victoria and University of Victoria and National Research Council Canada and University of Victoria and University of Victoria and University of Saskatchewan and University of Victoria
• Keywords: graph mining; core decomposition; COVID-19; data mining; graph theory; network inference

Abstract

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. 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 (mainly proteins with their encoding genes). 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.