Volume 10 Number 2 2026
Network Medicine Approaches to Long COVID: Integrating Multi-Omic Data to Redefine Disease Taxonomy
Gnanasekaran Ashok 1, Mohammed Shahjahan Kabir 2, Lubna Shirin 3, Syeda Humayra 4, Taha Sulayman 5, Keichiro Mihara 6
Integrative Biomedical Research 10 (2) 1-8 https://doi.org/10.25163/biomedical.10210969
Submitted: 24 October 2026 Revised: 11 December 2026 Accepted: 21 December 2026 Published: 23 December 2026
Long COVID, or post-acute sequelae of SARS-CoV-2 infection, now affects a substantial minority of people who survive the acute illness, yet it is still recorded in most health systems under a single, rather blunt diagnostic code. That code has been useful for counting patients; it has been far less useful for understanding them. In this review, we ask whether network medicine—an approach that treats disease as a disturbance of interconnected molecular systems rather than a failure of one organ—can offer a better way to describe, stratify, and eventually treat the condition. We searched PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar and synthesized 26 sources spanning host–pathogen interactomics, bulk and single-cell transcriptomics, metabolomics, machine learning applied to electronic health records, and network-based drug repurposing. Across these studies, a fairly coherent picture emerges, although it is assembled largely from acute-phase data. Viral host targets cluster into a connected module of the human interactome, and signals appear to propagate from that module toward tissue-specific neighborhoods, including neurovascular, pulmonary-vascular, and intestinal ones. Unsupervised learning on large clinical datasets, meanwhile, separates patients coded as having long COVID into cardiopulmonary, neurological, gastrointestinal, upper-respiratory, and age-related comorbid endotypes. Proximity-based and graph-learning pipelines have prioritized repurposable drugs and rational combinations, with experimental hit rates well above those of unguided screening. What is still missing is direct longitudinal multi-omic evidence from people with long COVID. We argue that an endotype-based taxonomy is within reach, provided that computational predictions are tested prospectively and within randomized trials.
Keywords: long COVID; post-acute sequelae of SARS-CoV-2; network medicine; multi-omics; human interactome; machine learning; drug repurposing; disease taxonomy
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