A Machine-Learning Approach to Finding Gene Target Treatment Options for Long COVID

Abstract

Long COVID, also known as post-acute sequelae of SARS-CoV-2 infection (PASC), encompasses a range of symptoms persisting for weeks or months after the acute phase of COVID-19. These symptoms, affecting multiple organ systems, significantly impact the quality of life. This study employs a machine-learning approach to identify gene targets for treating Long COVID. Using datasets GSE275334, GSE270045, and GSE157103, Recursive Ensemble Feature Selection (REFS) was applied to identify key genes associated with Long COVID. The study highlights the therapeutic potential of targeting genes such as PPP2CB, SOCS3, ARG1, IL6R, and ECHS1. Clinical trials and pharmacological interventions, including dual antiplatelet therapy and anticoagulants, are explored for their efficacy in managing COVID-19-related complications. The findings suggest that machine learning can effectively identify biomarkers and potential therapeutic targets, offering a promising avenue for personalized treatment strategies in Long COVID patients.