Exploring Molecular Mechanisms Through Graph Convolutional Neural Network for Protein Profiling

Using Graph Convolutional Networks (GCNs) for extensive protein profiling, this research aims to unravel the molecular subtleties of COVID-19. The main objective is to decipher the intricate relationship between viral and human host proteins to have a better grasp of disease path physiology and possible treatment options. The goal of this study is to use GCNs to identify structural motifs, functional pathways, and critical protein-protein interactions that are involved in viral infection and the control of the host immune response. The study’s overarching goal is to aid in the creation of better diagnostic instruments, therapeutic approaches, and vaccinations against COVID-19 by clarifying these molecular pathways. By using multi-omics data, this study aims to improve GCNs' predictive power in defining new protein interactions and finding druggable targets. This study aims to speed up the identification of much-needed antiviral medicines by using cutting-edge computational approaches to shed light on the path physiology of COVID-19. The long-term objective is to address the continuing worldwide health catastrophe caused by the COVID-19 pandemic by creating a collaborative platform for multidisciplinary research that brings together computational biologists and experimental virologists. Using a variety of criteria, the Kaggle database shows (i) how two cities lived during COVID-19. (i) Bike_ID, Rental_ID, Duration in hours (min 60, max 1380), Start Station ID, which can be anything from one day to three or four days in the future, with a unique range of values from 0.0927542 to -0.197574, Latitude from 51.506 to 51.4996, and (iii) Transactions done during COVID-19 with a range of values from 1494460 to 232629023, with a class type of 1,2, or unknown, and a duration in hours (min 60, max 1380).