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 toward 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 toward the discovery of drugs. In this study, an approach was presented for predicting novel interactions from maximal biclusters. Additionally, the predicted interactions are verified from biological perspectives. For this, a study was conducted on the gene ontology and KEGG-pathway in relation to the newly predicted interactions.
Highlights
Protein–protein interactions (PPI) denote a complex network of reactions that take the responsibility to synchronize and execute the biological process at the cell level in all organisms
Potential viral–host protein interactions can be identified which is known as crucial information toward the discovery of drugs
SARS-CoV-2-Human PPI dataset is denoted as HxC where the rows represent the human proteins and the columns represent the viral proteins
Summary
Protein–protein interactions (PPI) denote a complex network of reactions that take the responsibility to synchronize and execute the biological process at the cell level in all organisms. A good classifier needs proper exploitation of both positive and negative interaction data This kind of barrier directs us to follow the approach of rule mining-based methodology which has been successfully implemented in the research [2]. Summarizing the above discussion, in this study, bimax biclustering approach has been followed on the manually curated protein–protein interaction dataset of SARS-COV-2 and human protein. It has been shown the procedure used for predicting interactions from biclusters and from the generated rules as well. The input dataset is given as supplementary material along with this article
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