Omics-Based Computational Approaches for Biomarker Identification, Prediction, and Treatment of Long COVID

Abstract

Long COVID, also referred to as post-acute sequelae of COVID-19 (PASC), is a substantial global health concern estimated to have affected over 145 million individuals worldwide. Characterized by persistent and new symptoms extending beyond four weeks from the initial infection--such as fatigue, breathlessness, and cognitive impairments--Long COVID poses significant challenges to healthcare systems due to its chronic nature and diverse clinical manifestations. An urgent need exists to elucidate its underlying mechanisms to facilitate the development of effective diagnostic tools and targeted treatments. This paper provides a comprehensive overview of the current datasets and computational methods to investigate the causes, risk factors, and potential treatments for Long COVID. To understand better the molecular processes driving this condition, we examine various omics data sources, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics datasets. Moreover, we discuss how integrating multi-omics data and using advanced computational techniques--such as machine learning and network analysis--can enhance Long COVID diagnosis, prognosis, and therapeutic strategies. We also emphasize the importance of larger, more diverse cohorts, longitudinal research designs, and cross-cohort validations to strengthen the reliability and applicability of findings. By addressing these crucial aspects, this review aims to advance the development of effective interventions for Long COVID, ultimately improving patient outcomes and alleviating the disease burden.