Finding Prediction of Interaction between SARS-CoV-2 and Human Protein: A Data Driven Approach

Abstract

COVID-19 pandemic defined a worldwide health crisis into a humanitarian crisis. Amid this global emergency, human civilization is under enormous strain since no proper therapeutic method is discovered yet. A wave of research effort has been put towards the invention of therapeutics and vaccines against COVID-19. Contrarily, the spread of this fatal virus has already infected millions of people and claimed many lives all over the world. Computational biology can attempt to understand the protein-protein interactions between the viral protein and host protein. Therefore potential viral-host protein interactions can be identified which is known as crucial information towards the discovery of drugs. In this paper, we have presented an approach for predicting novel interactions from maximal biclusters. Additionally, the predicted interactions are verified from biological perspectives. For this, we conduct a study on the gene ontology and KEGG pathway in relation to the newly predicted interactions.

Using unsupervised learning algorithms to identify essential genes associated with SARS-CoV-2 as potential therapeutic targets for COVID-19

“…Figure 5 contains six submodules with a significant p-value from the DAVID tool. We found that these modules have been identified in various studies related to COVID-19 Ghosh et al (2021); Hachim et al (2020).…”

Knowledge Discovery of Sundarban Mangrove Species: A Way Forward for Managing Species Biodiversity

“…Bi-clustering may be viewed as a two-dimensional clustering approach [22] where the subsets of both rows and columns of a two-dimensional data matrix are grouped. Biclustering has been successfully applied in bioinformatics studies for gene-expression data analysis, protein-protein interaction data analysis [14]. In ecology, biclustering is used for assessing the variation in birds' population in response to the change in local weather and habitat [1].…”