Integrative Biomedical Research

1. Introduction

When the COVID-19 pandemic began, nearly all clinical attention went, understandably, to the lungs and to the first few weeks of illness. It took some time for a quieter problem to come into view. A meaningful share of people infected with SARS-CoV-2—estimates vary, but figures of up to around 15% of survivors have been reported—continue to experience symptoms that persist, relapse, or appear for the first time months and sometimes years after the initial infection (Coste et al., 2025; Zeraatkar et al., 2025). The condition goes by several names: post-acute sequelae of SARS-CoV-2 infection (PASC), post-COVID-19 condition, or, most commonly in everyday use, long COVID (Pfaff et al., 2023; Zeraatkar et al., 2025).

What makes long COVID so difficult is not only its frequency but its variety. Patients describe profound fatigue, cognitive slowing often called “brain fog,” post-exertional malaise, muscle pain, breathlessness that lingers, autonomic instability, gastrointestinal complaints, and cardiovascular problems (Pfaff et al., 2023; Zeraatkar et al., 2025; Zhou et al., 2020b). Two people carrying the same diagnosis may, in practice, share very few of these features. That observation alone should make us cautious about treating long COVID as one disease.

The biological explanations proposed so far are numerous, and they are not mutually exclusive. They include low-level viral persistence in tissue reservoirs, autoantibody-driven autoimmunity, endothelial injury with microvascular thrombosis, lingering neuroinflammation, and a host immune response that never quite returns to baseline (Pavel et al., 2021; Zeraatkar et al., 2025; Zhou et al., 2020b). Each mechanism has some supporting evidence; none, on its own, seems to account for the whole clinical picture.

Clinical classification, however, has so far moved in the opposite direction—toward simplification. In the United States, the ICD-10-CM code U09.9 (“Post COVID-19 condition, unspecified”) has become the principal way of flagging the condition in electronic health records (EHRs) (Pfaff et al., 2023). The code has undeniable practical value for identifying cohorts. Yet, as Pfaff et al. (2023) showed, grouping everyone under U09.9 tends to hide the very physiological differences that matter for choosing treatments, stratifying patients, and designing trials. Symptom-based definitions, in other words, may be adequate for surveillance but are probably too coarse for mechanism-driven care (Halamka et al., 2020; Pfaff et al., 2023).

If the problem is that long COVID spans many organs and many pathways, then a framework built around interconnection seems a reasonable place to look. Network medicine applies ideas from network science and systems biology to the human interactome—the web of physical and functional interactions among molecules in the cell (Barabási et al., 2011; Conte et al., 2020; Halamka et al., 2020). Its central claim is that diseases are rarely the result of a single broken component. Instead, they tend to show up as perturbations concentrated in particular neighborhoods of the interactome, often called disease modules (Conte et al., 2020; Morselli Gysi et al., 2021; Santos et al., 2022). For viral infections, this idea has a natural extension: viral proteins bind host proteins, frequently at well-connected hubs, and in doing so they can reshape entire host pathways, forming what might be described as a virus–host disease module (Morselli Gysi et al., 2021; Santos et al., 2022; Zhou et al., 2020a).

Over the last few years, it has become feasible to layer several kinds of molecular data onto this network—genomic variants, bulk and single-cell transcriptomes, proteomes, metabolomes, and even environmental exposures (Halamka et al., 2020; Tomazou et al., 2021). When SARS-CoV-2 protein interactors and downstream differentially expressed genes are placed on the same map, intermediate genes and tissue-specific perturbation patterns begin to emerge (Pavel et al., 2021; Verstraete et al., 2020; Zhou et al., 2020b). Some of these analyses point to shared sub-networks between COVID-19 and chronic conditions such as inflammatory bowel disease, pulmonary fibrosis, cardiovascular disease, and Alzheimer’s disease (Pavel et al., 2021; Zhou et al., 2020b). This offers at least a plausible account of how an infection that enters through receptors such as ACE2 and TMPRSS2 in one tissue might go on to influence distant organ systems (Halamka et al., 2020; Zhou et al., 2020b).

Interactome maps, however elegant, do not by themselves tell a clinician what to do. Bridging that gap has increasingly fallen to machine learning (Halamka et al., 2020; Jamshidi et al., 2020; Santos et al., 2022). Unsupervised methods—K-means, self-organizing maps (SOMs), and community-detection algorithms—alongside random forests and graph neural networks (GNNs), are well suited to finding patterns that were not specified in advance in high-dimensional clinical and molecular data (Halamka et al., 2020; Ilbeigipour et al., 2022; Sadegh et al., 2021).

One of the more striking demonstrations comes from the National COVID Cohort Collaborative (N3C). By applying the Louvain community-detection algorithm to networks of co-occurring diagnoses, Pfaff et al. (2023) found that patients coded with long COVID did not form one homogeneous group; they separated into clusters that could be read as clinical endotypes—neurological, cardiopulmonary, gastrointestinal, upper-respiratory, and age-related comorbid patterns, each with a somewhat different age profile. Population-level network modeling adds a further layer, suggesting that risk is shaped by an intertwined set of pre-existing comorbidities, socioeconomic circumstances, working conditions, and the severity of the acute illness (Coste et al., 2025). Taken together, these strands hint that a taxonomy grounded in biological endotypes, rather than in a single code, may be achievable (Halamka et al., 2020; Pfaff et al., 2023).

Therapeutics present a related challenge. Developing new drugs de novo takes many years, which is a long time for patients who are already unwell. Repurposing existing agents is therefore attractive (Li et al., 2021; Morselli Gysi et al., 2021; Zhou et al., 2020a). Network-based repurposing asks a simple question: how close, in the topology of the interactome, are a drug’s targets to the proteins perturbed by disease? (Conte et al., 2020; Morselli Gysi et al., 2021; Zhou et al., 2020a). Tools built on this idea—SAveRUNNER, graph kernels, network propagation, and multi-relational graph embeddings—rank candidate compounds by their expected ability to push a disturbed module back toward a healthier state (Conte et al., 2020; Fiscon et al., 2021; Sadegh et al., 2021; Santos et al., 2022; Verstraete et al., 2020).

Because a multi-system disorder is unlikely to yield to a single target, network pharmacology also offers a principled way to design combinations (Santos et al., 2022; Zhou et al., 2020a). The so-called Complementary Exposure pattern proposes that two drugs work best together when each reaches the disease module but through separate, non-overlapping neighborhoods, which in theory maximizes coverage while limiting shared-target toxicity (Zhou et al., 2020a). Multilayer knowledge graphs such as CovMulNet19 and CoREx, together with patient-generated data mined from online communities, may help match specific endotypes to specific regimens (Koss & Bohnet-Joschko, 2022; Santos et al., 2022; Tomazou et al., 2021; Verstraete et al., 2020).

Against this background, the review has four aims. First, we synthesize multi-omic evidence—bulk and single-cell transcriptomics, proteomics, metabolomics, and interactomics—on how SARS-CoV-2 perturbs host systems in pulmonary, cardiovascular, neurological, and gastrointestinal tissues. Second, we examine the network algorithms (proximity measures, graph kernels, random walk with restart, and module-detection frameworks) used to define virus–host disease modules. Third, we assess how unsupervised learning applied to large EHR resources and epidemiological cohorts might move the field from a descriptive code such as U09.9 toward a mechanistic, endotype-based taxonomy. Fourth, we review network-based repurposing and combination-design pipelines and consider which candidates, if any, are ready for clinical testing in defined long COVID subgroups.

2. Network Medicine and Machine Learning for Long COVID Phenotyping and Drug Repurposing

2.1 Heterogeneity as the Central Problem

It is worth pausing on how the field arrived here. Early in the pandemic, trials and treatment protocols concentrated, reasonably, on acute respiratory failure and hyperinflammatory cytokine release. Only gradually did it become clear that a considerable proportion of infected people—up to 15% by some estimates—were left with long-lasting and sometimes disabling physical and cognitive sequelae (Zeraatkar et al., 2025). The symptom spectrum is wide: post-exertional malaise, chronic fatigue, dyspnea, autonomic dysregulation, gastrointestinal disturbance, and cognitive impairment all feature prominently (Pfaff et al., 2023; Zeraatkar et al., 2025).

The difficulty, as several authors have argued, lies partly in how the condition is classified. Diagnostic frameworks that rely on one code, such as ICD-10-CM U09.9, make it easy to count cases but hard to see differences between them (Pfaff et al., 2023). Work in the N3C database illustrates this tension quite clearly: U09.9 supports high-level cohort tracking, but it gathers people with very different organ involvement, symptom trajectories, and presumably different underlying drivers under one label (Pfaff et al., 2023). This, in turn, may be one reason why precision-medicine efforts and trial designs have struggled to gain traction (Halamka et al., 2020; Pfaff et al., 2023).

Systems biology offers an alternative lens. Network medicine views disease as an emergent property of perturbed biological networks rather than as an isolated defect in one cell type or organ (Barabási et al., 2011; Halamka et al., 2020). Within this view, perturbations tend to concentrate in local neighborhoods of the interactome—disease modules (Barabási et al., 2011; Conte et al., 2020). For a post-viral syndrome, the implication is fairly intuitive, even if it is not yet proven: alterations in host–pathogen interactions could spread dysregulation across several tissue-specific sub-networks, which would help explain why a localized viral entry event can end in a systemic, multi-organ illness (Coste et al., 2025; Halamka et al., 2020; Zhou et al., 2020b). Figure 1 sketches the overall framework we use to organize the rest of this review (Figure 1).

2.2 The Host–Pathogen Interactome and Its Tissue Footprint

2.2.1 Mapping the virus–host disease module

Understanding the molecular architecture of long COVID depends on combining datasets generated at different resolutions—bulk, single-cell, and single-nucleus. Multi-omic profiles that include genomic variants, RNA sequencing, liquid chromatography–mass spectrometry (LC-MS) proteomics, and serum metabolomics have been used to chart how viral proteins engage host machinery (Pavel et al., 2021; Tomazou et al., 2021; Zhou et al., 2020b). A key starting point was the affinity-purification mass spectrometry map produced by Gordon et al. (2020), which identified 332 high-confidence human proteins bound by SARS-CoV-2 proteins. When these proteins were projected onto the human interactome, most of them fell into a single large connected component, forming what Morselli Gysi et al. (2021) treated as the core virus–host disease module; Verstraete et al. (2020) reached a broadly similar picture using a multilayer representation. Messina et al. (2020) and Stolfi et al. (2020) likewise used interactome-based models to trace how viral–host contacts extend into wider host pathways.

2.2.2 Pulmonary, gastrointestinal, and metabolic signals

Mapping across tissues has revealed perturbations in several anatomical compartments (Pavel et al., 2021; Stolfi et al., 2020; Tomazou et al., 2021). In the airways and the gut, single-cell data show that the main entry receptors, ACE2 and TMPRSS2, are co-expressed in particular cell populations—airway club cells, type II pneumocytes, and absorptive enterocytes, including those in inflamed ileal tissue (Halamka et al., 2020; Zhou et al., 2020b). In Crohn’s disease models, raised enterocyte co-expression points toward shared sub-networks between viral biology and chronic intestinal inflammation (Zhou et al., 2020b). Metabolic data add another dimension: integrated transcriptomic and metabolomic analyses reported reduced L-arginine and L-citrulline and disturbed arachidonate signaling, patterns that resemble those seen in chronic airway disease such as asthma (Zhou et al., 2020b).

Beyond direct interactors, multi-scale integration using resources such as the Unified Knowledge Space (UKS) suggests that host-response genes one or two steps removed from the virus may be the ones that drive longer-term tissue change (Pavel et al., 2021). These intermediate genes—pro-angiogenic factors such as VEGF and IL-6, and connective-tissue remodeling pathways—could plausibly link acute immune activation to pulmonary vascular endothelialitis, microthrombosis, and fibrosis (Pavel et al., 2021).

2.2.3 The brain: an indirect route to injury

The central nervous system provides perhaps the clearest example of how network analysis can reframe a clinical question. Single-nucleus transcriptomic data across human brain regions show very low baseline neuronal expression of ACE2 and TMPRSS2, which argues against frequent direct neuroinvasion (Zhou et al., 2021). Network proximity analyses, however, indicate that SARS-CoV-2 host factors overlap substantially with modules for neuroinflammation and brain microvascular injury (Zhou et al., 2021). Alternative entry and docking factors—basigin (BSG), furin (FURIN), and neuropilins 1 and 2—together with innate antiviral genes (LY6E, IFITM2, IFITM3, IFNAR1), are expressed at higher levels in brain microvascular endothelial cells and microglia (Zhou et al., 2021). Changes in Alzheimer’s disease biomarkers in cerebrospinal fluid and blood from COVID-19 patients, including TGFB1, SPP1, CXCL10, and TNFRSF1B, further suggest that post-viral cognitive dysfunction may arise from endothelial injury and neuroinflammatory cascades rather than from direct destruction of brain tissue (Zhou et al., 2021). The way these organ-specific signals may fan out from a common interactome module is summarized in Figure 2 (Figure 2); the underlying data layers are compiled in Table 1 (Table 1).

2.3 Machine Learning and Computable Phenotyping

2.3.1 Community detection in large EHR networks

Turning network biology into clinical tools requires methods that can cope with very large, messy real-world datasets (Halamka et al., 2020; Jamshidi et al., 2020; Pfaff et al., 2023). Unsupervised learning is attractive here precisely because it does not assume the categories in advance (Ilbeigipour et al., 2022; Pfaff et al., 2023). In the N3C enclave, which holds records for more than 16 million patients, Pfaff et al. (2023) built networks of diagnoses co-occurring within 0–60 days of a long COVID index date and applied Louvain community detection. The result was a set of age-stratified clusters. A cardiopulmonary pattern

Figure 1. A network medicine framework for moving long COVID from a single diagnostic code to a taxonomy based on disease mechanisms The single ICD-10-CM code U09.9 is useful for identifying patients but hides differences in which organs are involved and what drives the disease. Genomic, transcriptomic, proteomic, metabolomic, and clinical data are mapped onto the human interactome. Network algorithms and machine learning then turn that map into disease modules and patient clusters. The outputs are clinical endotypes, readouts of the underlying mechanisms, and network-guided treatment hypotheses. The dashed arrow shows how prospective cohorts and randomized trials feed back to refine the classification.

Figure 2. Proposed spread of SARS-CoV-2 host–pathogen disturbances through the human interactome into organ-specific sub-networks Viral proteins bind 332 human host proteins through primary receptors (ACE2, TMPRSS2) and alternative docking factors (BSG, FURIN, NRP1). These host targets form one connected disease module. Intermediate genes such as IL6, VEGF, and ICAM1 appear to carry the disturbance into the lungs and blood vessels, heart, gut, and brain vasculature. The organ-level changes shown may together account for the multi-organ endotypes of long COVID. Most of the links shown come from studies of acute infection and remain hypotheses for long COVID.

featured chest pain, dyspnea, palpitations, tachycardia, and exercise intolerance. A neurological and cognitive pattern was dominated by chronic fatigue, brain fog, headache, sleep disturbance, and myalgic encephalomyelitis/chronic fatigue syndrome-like presentations. A gastrointestinal pattern—abdominal pain, nausea, diarrhea, and metabolic disturbance—appeared particularly among people younger than 21 years. An upper-respiratory pattern included persistent anosmia, dysgeusia, rhinitis, and pharyngitis (Halamka et al., 2020; Pfaff et al., 2023). Finally, an age-related comorbid pattern, concentrated in adults aged 65 and older, involved worsening of existing cardiovascular, metabolic, and neurodegenerative conditions such as heart failure and type 2 diabetes (Pfaff et al., 2023).

2.3.2 Clustering clinical trajectories

Graph-based methods are not the only option. K-means, X-means, and SOM neural networks have been used to group patients by clinical variables, laboratory biomarkers, and acute illness trajectories (Benito-León et al., 2021; Ilbeigipour et al., 2022). SOMs, for instance, project inputs such as age, length of hospital stay, intensive care admission, intubation history, and inflammatory marker levels onto a two-dimensional grid (Ilbeigipour et al., 2022). These analyses show that older age and higher inflammatory markers track closely with more severe courses—yet they also show that symptom severity and organ involvement vary a good deal within the same demographic strata (Ilbeigipour et al., 2022). Benito-León et al. (2021) similarly found severity subgroups that were not explained by age or sex alone, which is a small but useful reminder that demographics are an imperfect proxy for biology.

2.3.3 Language models and population networks

Natural language processing (NLP) has opened another door. Deep-learning-augmented curation platforms such as nferX can extract concepts from millions of free-text physician notes (Halamka et al., 2020). When this narrative information is triangulated with structured laboratory data and single-cell expression profiles across roughly 25 human tissues, subtle, uncoded features—early loss of smell or taste, for example—appeared to be better early predictors of outcome than fever or cough (Halamka et al., 2020). At the population level, Coste et al. (2025) modeled long COVID as sitting within a multidimensional web of acute severity, chronic comorbidity, social determinants, and occupational exposure. The combined phenotyping pipeline is illustrated in Figure 3 (Figure 3), and the individual methods are compared in Table 2 (Table 2).

2.4 Network-Based Drug Repurposing

2.4.1 The proximity hypothesis

Because conventional drug development can take a decade or more, network medicine offers a faster, if less certain, route through repurposing (Fiscon et al., 2021; Morselli Gysi et al., 2021; Zhou et al., 2020a). By estimating how approved drugs perturb host networks, algorithms can screen thousands of compounds relatively quickly (Li et al., 2021; Morselli Gysi et al., 2021; Santos et al., 2022). Most of these approaches rest on the network proximity hypothesis: a drug is more likely to be useful if its targets lie within, or close to, the disease module (Fiscon et al., 2021; Morselli Gysi et al., 2021; Stolfi et al., 2020). Proximity is commonly quantified as the average shortest-path distance between a drug’s target set T and the disease protein set S:

dT,S=1/Tt∈T​mins∈Sdt,s

where d(t, s) is the shortest path length between target t and disease protein s in the reference interactome, usually converted to a z-score against randomized target sets (Fiscon et al., 2021; Zhou et al., 2021). Li et al. (2021) provide a concrete example: starting from 34 disease-related genes and a network of 1,344 genes across 24 enriched pathways, their proximity analysis yielded 78 candidates, narrowed by expert review to 30.

2.4.2 Algorithms beyond simple distance

Several algorithms refine this basic idea. SAveRUNNER scores drug–disease pairs with a network similarity measure adjusted for cluster quality and sigmoidal normalization; applied to virus–host networks, it prioritized off-label candidates including anti-inflammatory agents, central nervous system modulators, histamine-receptor antagonists, and sodium-channel blockers (Fiscon et al., 2021). Random walk with restart (RWR) and related propagation methods simulate a walker that moves along interactome edges from disease seed nodes, returning to the seeds with a fixed probability; the resulting visitation scores help identify indirect host targets and functional sub-networks altered by infection (Fiscon et al., 2021; Messina et al., 2020; Stolfi et al., 2020). Graph neural networks and knowledge-graph embeddings go a step further by attempting to capture global topology (Hsieh et al., 2021; Santos et al., 2022). Hsieh et al. (2021), for example, built a COVID-19 knowledge graph linking viral proteins, host genes, pathways, drugs, and phenotypes, learned drug representations with a deep graph model initialized from a general biomedical knowledge graph, and then checked candidates against gene-set enrichment, in vitro screening data, and EHR-based treatment effects. Integrated platforms such as NeDRex combine module-detection methods (DIAMOnD, BiCoN) with drug-ranking methods (TrustRank, closeness centrality) in an expert-in-the-loop environment (Sadegh et al., 2021).

2.4.3 Consensus and the “network drug” observation

No single algorithm seems to capture everything. Recognizing this, Morselli Gysi et al. (2021) fused rankings from multiple pipelines—AI-based, diffusion-based, and proximity-based—into a consensus. Their rank-aggregation approach reportedly reached a hit rate of up to 62% in cell-based viral inhibition assays, compared with roughly 0.8% for unguided screening (Morselli Gysi et al., 2021). Perhaps the more interesting observation is that the great majority of active “network drugs” (over 95%) did not bind viral proteins at all; they acted on host proteins in the neighborhood of the disease module (Morselli Gysi et al., 2021). If that finding generalizes, it would support a strategy of modulating host sub-networks—dampening neuroinflammation, protecting endothelium—rather than chasing the virus alone (Morselli Gysi et al., 2021; Zhou et al., 2021). The full repurposing pipeline is depicted in Figure 4A (Figure 4), and the algorithms are compared in Table 3 (Table 3).

2.5 Network Pharmacology, Combinations, and Patient-Generated Evidence

2.5.1 Complementary Exposure

Because long COVID involves several pathways at once, single-target monotherapy may simply be insufficient (Santos et al., 2022; Zhou et al., 2020a). Network pharmacology offers a rational way to combine drugs so that effects add up while toxicities do not (Hsieh et al., 2021; Zhou et al., 2020a). The Complementary Exposure pattern formalizes this (Zhou et al., 2020a). Two drugs A and B are considered promising partners if both target sets are proximal to the disease module (z_{T_A,S} < 0 and z_{T_B,S} < 0) and, at the same time, their targets occupy separate neighborhoods, reflected by a positive separation score:

sAB=⟨dAB⟩-⟨dAA⟩+⟨dBB⟩/2>0

Here ⟨d_AB⟩ is the mean shortest distance between the targets of A and B, and ⟨d_AA⟩ and ⟨d_BB⟩ describe the internal spread of each target set (Zhou et al., 2020a). In principle, such a pair perturbs different arms of the same module without piling pressure onto the same cellular targets (Figure 4B). Using this logic, Zhou et al. (2020a) proposed pairings such as sirolimus with dactinomycin, mercaptopurine with melatonin, and toremifene with emodin, while Hsieh et al. (2021) highlighted etoposide with sirolimus and hydroxychloroquine with melatonin as complementary combinations.

2.5.2 Knowledge graphs and crowdsourced hypotheses

Interactive resources try to close the distance between prediction and practice (Koss & Bohnet-Joschko, 2022; Santos et al., 2022; Verstraete et al., 2020). CovMulNet19 integrates viral and human proteins, Gene Ontology terms, related diseases, symptoms, and candidate compounds in a single multilayer network, so that molecular perturbations can be read alongside clinical manifestations (Verstraete et al., 2020). CoREx provides a web environment for exploring drug perturbation scores, Connectivity Map (CMAP) expression-reversal signatures, and drug–target relationships within host sub-networks (Santos et al., 2022). Network pharmacology has also been applied to traditional medicine: Sun et al. (2020) combined frequency and association-rule mining of 173 historical prescriptions with network analysis and found considerable overlap with Lianhua Qingwen capsules and Ma Xing Shi Gan decoction, although they stressed the need for experimental confirmation.

Finally, social media mining may complement formal trials by capturing what patients are already trying. Koss and Bohnet-Joschko (2022) applied named-entity recognition and co-occurrence network analysis to posts in a large long COVID community on Reddit, extracting reported use of off-label medicines and supplements. Mapping such crowdsourced candidates back onto host networks could generate hypotheses that are patient-centered—though, of course, self-report is no substitute for controlled evaluation (Koss & Bohnet-Joschko, 2022; Zeraatkar et al., 2025). These platforms and combination models are summarized in Table 4 (Table 4).

3. Methods

3.1 Review Design and Rationale

We conducted a structured narrative review with a systematic search component. A formal meta-analysis did not seem appropriate: the included studies differ widely in data type (interactomic, transcriptomic, clinical, computational), in outcome (network metrics, cluster membership, experimental hit rates), and in population (acute COVID-19, post-acute cohorts, general populations). We therefore aimed for a transparent, reproducible search and extraction process, followed by a qualitative, theme-based synthesis. The review question was framed as follows: in people with SARS-CoV-2 infection and its post-acute sequelae, how have network medicine, multi-omic integration, and machine learning been used to (a) characterize disease mechanisms, (b) stratify patients into subphenotypes, and (c) prioritize therapeutic candidates?

3.2 Information Sources and Search Strategy

Four bibliographic sources were searched: PubMed/MEDLINE, Scopus, Web of Science Core Collection, and Google Scholar (the first [n] results, sorted by relevance). The search window ran from January 2011—the year in which the network medicine framework was consolidated in a widely cited review (Barabási et al., 2011)—to the date of the final search [authors to insert date]. Reference lists of included articles were screened by hand, and forward citation tracking was performed for key papers (Gordon et al., 2020; Morselli Gysi et al., 2021; Pfaff et al., 2023; Zhou et al., 2020a).

The PubMed strategy combined three concept blocks using Boolean operators, adapted for the syntax of each database:

Block 1 (condition): (“COVID-19”[MeSH] OR “SARS-CoV-2”[MeSH] OR “post-acute COVID-19 syndrome”[MeSH] OR “long COVID”[tiab] OR “PASC”[tiab] OR “post-COVID-19 condition”[tiab] OR “U09.9”[tiab])

Block 2 (approach): (“network medicine”[tiab] OR “interactome”[tiab] OR “protein interaction maps”[MeSH] OR “systems biology”[MeSH] OR “multi-omics”[tiab] OR “transcriptome”[MeSH] OR “proteomics”[MeSH] OR “metabolomics”[MeSH] OR “machine learning”[MeSH] OR “unsupervised machine learning”[MeSH] OR “cluster analysis”[MeSH] OR “natural language processing”[MeSH] OR “graph neural network”[tiab])

Block 3 (application, optional filter): (“drug repositioning”[MeSH] OR “drug repurposing”[tiab] OR “network pharmacology”[tiab] OR “phenotype”[tiab] OR “endotype”[tiab] OR “subphenotype”[tiab] OR “taxonomy”[tiab])

Blocks 1 and 2 were combined with AND; Block 3 was added in a second run to improve precision. No language filter was applied at the search stage, but only English-language full texts were retained.

3.3 Eligibility Criteria

Studies were eligible if they (i) concerned SARS-CoV-2 infection, COVID-19, or its post-acute sequelae; (ii) used a network-based, multi-omic, or machine learning method as a central analytic component; and (iii) reported findings

Figure 3. Machine learning pipeline for sorting patients with long COVID into computable subgroups Structured health-record codes, free-text clinical notes, hospital laboratory data, and population surveys are the four data sources. Each is analysed with a matching method: community detection, natural language processing, self-organizing maps with K-means clustering, or risk-factor network modelling. The analyses sort patients into five endotypes that differ by age: cardiopulmonary, neurological/cognitive, gastrointestinal, upper respiratory, and age-related comorbid. The coloured bars mark each endotype. Whether these clusters hold up in other health systems still needs to be tested.

Figure 4. Network-based drug repurposing pipeline (A) and the Complementary Exposure model for drug combinations (B) (A) Drug targets are placed on the interactome next to the virus–host disease module and scored by network proximity. The scores come from several algorithms, and their rankings are combined into a consensus before laboratory and population validation. (B) Two drugs whose targets each reach the disease module (z < 0) but sit in separate neighbourhoods (s_AB > 0) are expected to cover more of the disease with less overlapping toxicity. The schematic is illustrative and not drawn to scale.

relevant to mechanism, patient stratification, or therapeutic prioritization. Peer-reviewed original research, computational method papers with COVID-19 applications, and registered protocols for systematic reviews of long COVID interventions were accepted. Two foundational theoretical reviews of network medicine were retained to define core concepts (Barabási et al., 2011; Conte et al., 2020). We excluded editorials, conference abstracts without full text, studies that used network or machine learning terminology without a reproducible method, and purely imaging-based diagnostic classifiers that did not address mechanisms or subphenotypes.

3.4 Study Selection

After removal of duplicates in a reference manager, titles and abstracts were screened against the eligibility criteria, followed by full-text assessment. Screening was performed independently by two reviewers; disagreements were resolved by discussion and, where necessary, by a third reviewer. The number of records identified, screened, excluded (with reasons), and included at each stage should be reported in a flow diagram [authors to insert counts]. The final evidence base comprised 26 sources: 2 conceptual reviews of network medicine (Barabási et al., 2011; Conte et al., 2020), 12 studies of interactome construction, multi-omic integration, or network-based repurposing (Fiscon et al., 2021; Gordon et al., 2020; Hsieh et al., 2021; Li et al., 2021; Messina et al., 2020; Morselli Gysi et al., 2021; Pavel et al., 2021; Sadegh et al., 2021; Santos et al., 2022; Stolfi et al., 2020; Tomazou et al., 2021; Verstraete et al., 2020), 3 studies from one research group linking interactome biology to organ-specific manifestations and combination therapy (Zhou et al., 2020a, 2020b, 2021), 6 clinical, epidemiological, or machine learning phenotyping studies (Benito-León et al., 2021; Coste et al., 2025; Halamka et al., 2020; Ilbeigipour et al., 2022; Jamshidi et al., 2020; Pfaff et al., 2023), 2 studies of alternative hypothesis-generation sources (Koss & Bohnet-Joschko, 2022; Sun et al., 2020), and 1 living systematic review protocol (Zeraatkar et al., 2025).

3.5 Data Extraction

A standardized extraction form was piloted on a small subset of studies and then applied to all included sources. For each study we recorded: bibliographic details; study aim; data type and resolution (for example, bulk RNA sequencing, single-cell or single-nucleus RNA sequencing, affinity-purification mass spectrometry, LC-MS proteomics, metabolomics, structured EHR codes, or unstructured clinical text); source dataset and sample size; reference interactome or knowledge graph (with node and edge counts where reported); algorithm and key parameters (for example, distance metric, restart probability, clustering resolution, or embedding architecture); validation strategy (in vitro assays, retrospective EHR analysis, or none); principal findings; and stated limitations. Extraction was performed by one reviewer and checked in full by a second. Where numerical values differed between the main text and supplementary material of a paper, the main text was used.

3.6 Appraisal of Methodological Quality

Given the heterogeneity of designs, no single appraisal tool was suitable. We instead assessed each study against five domains adapted to computational biomedicine: (1) transparency of data sources and preprocessing; (2) availability of code or software; (3) use of appropriate null models or randomization for network statistics; (4) presence of independent validation (experimental, clinical, or external dataset); and (5) relevance of the study population to long COVID specifically, as opposed to acute COVID-19. Each domain was rated as adequate, partially adequate, or not reported. These ratings informed the weight given to findings in the synthesis rather than serving as grounds for exclusion.

3.7 Synthesis

Findings were organized into four prespecified themes corresponding to the review aims: (i) multi-omic and tissue-level mechanisms; (ii) machine learning and computable phenotyping; (iii) network algorithms for disease-module discovery and drug repurposing; and (iv) network pharmacology, combination design, and crowdsourced discovery. Within each theme, studies were tabulated (Tables 1–4) and compared narratively, with attention to agreement, contradiction, and the extent to which conclusions drawn from acute COVID-19 could reasonably be extended to post-acute disease. Conceptual figures (Figures 1–4) were drawn by the authors from the extracted data to summarize the relationships between themes. No quantitative pooling was attempted, and reported performance metrics (such as AUC or experimental hit rates) are presented as reported by the original authors.

4. Synthesis of Findings: From Interactome Perturbation to Computable Endotypes and Network-Guided Therapeutics

4.1 Overview of the Evidence Base

Read together, the 26 sources do not describe long COVID as a single clinical entity. What they describe, instead, is a layered architecture in which acute perturbations of the host–pathogen interactome appear to propagate into persistent, tissue-specific sub-network dysfunction (Coste et al., 2025; Halamka et al., 2020; Pavel et al., 2021). Four broad findings stand out. Multi-omic mapping separates discrete tissue mechanisms, from neurovascular endothelial injury in the brain to pro-angiogenic remodeling in the lung (Pavel et al., 2021; Zhou et al., 2020b, 2021). Unsupervised learning on large EHR repositories resolves the generic U09.9 code into several age-stratified endotypes (Ilbeigipour et al., 2022; Pfaff et al., 2023). Network proximity, graph learning, and consensus pipelines prioritize host-directed repurposing candidates with notably better experimental yield than unguided screens (Fiscon et al., 2021; Morselli Gysi et al., 2021; Santos et al., 2022). And network pharmacology proposes combinations whose targets are complementary rather than redundant (Hsieh et al., 2021; Zhou et al., 2020a). A caveat applies throughout, and we return to it in the Discussion: most of the molecular evidence was generated in acute COVID-19, not in people with established long COVID.

4.2 Tissue-Specific Sub-Networks

4.2.1 Pulmonary and vascular remodeling

In the respiratory tract, bulk and single-cell RNA sequencing place ACE2 and TMPRSS2 co-expression mainly in airway club cells and type II pneumocytes (Zhou et al., 2020b). Yet persistent pulmonary symptoms—ongoing dyspnea, reduced diffusing capacity—seem more closely tied to downstream host gene networks than to entry factors themselves. Integrated network analyses identified upregulation of IL-6, VEGF, and ICAM1 as hubs linking inflammation to angiogenesis (Pavel et al., 2021). These hubs are plausibly involved in microthrombosis, endothelialitis, and extracellular matrix remodeling, which in a subset of patients might progress to interstitial fibrosis (Pavel et al., 2021; Zhou et al., 2020b) (Table 1; Figure 2).

4.2.2 Neurovascular injury and neuroinflammation

The central nervous system findings are, to our reading, among the most consistent. Neuronal expression of ACE2 and TMPRSS2 is negligible, making direct neuronal infection unlikely to be common (Zhou et al., 2021). Alternative entry factors (BSG/CD147, FURIN, NRP1) and innate immune genes (LY6E, IFITM2, IFITM3, IFNAR1) are instead enriched in brain microvascular endothelial cells and microglia (Zhou et al., 2021). Single-cell profiling of cerebrospinal fluid shows dedifferentiated monocytes, signs of T-cell exhaustion, and elevated VCAM1, compatible with blood–brain barrier disturbance (Zhou et al., 2021). Interactome proximity between the SARS-CoV-2 neuroinflammatory sub-network and Alzheimer’s disease modules, marked by TGFB1, SPP1, CXCL10, and TNFRSF1B, suggests a shared vascular-inflammatory route to cognitive symptoms (Zhou et al., 2021) (Table 1; Figure 2).

4.2.3 Gastrointestinal and systemic overlap

In gut tissue, ACE2 and TMPRSS2 are strongly co-expressed in absorptive enterocytes (Zhou et al., 2020b). Network overlap analyses indicate that the viral interactome intersects significantly with inflammatory bowel disease sub-networks involving NOD2, CARD9, and RIPK1 (Zhou et al., 2020b). This overlap offers a candidate explanation for post-acute abdominal pain and altered bowel habit, though it should be stressed that the supporting data are largely transcriptomic and cross-sectional (Table 1). At the systemic level, reduced L-arginine and L-citrulline and altered arachidonate metabolism suggest shared metabolic signatures with chronic airway disease (Zhou et al., 2020b), while multiplex integration of omic layers narrows candidate drug lists considerably (Tomazou et al., 2021).

4.3 Computable Phenotypes and Patient Stratification

4.3.1 EHR-based community detection

Applying Louvain community detection to co-occurring diagnoses in the N3C enclave, Pfaff et al. (2023) showed that patients carrying U09.9 cluster into distinguishable groups (Table 2; Figure 3). The cardiopulmonary cluster is dominated by chest pain, dyspnea, postural tachycardia, and exercise intolerance. The neurological and cognitive cluster features fatigue, brain fog, sleep disturbance, headache, and ME/CFS-like presentations. The gastrointestinal cluster, with abdominal pain, nausea, vomiting, and altered bowel habit, is over-represented among those under 21 years. The upper-respiratory

Table 1: Multi-Omic Data Layers, Cellular Receptors, and Pathobiological Mechanisms Across Target Tissues. This table synthesizes multi-scale omic datasets (bulk/single-cell/single-nucleus transcriptomics, LC-MS proteomics, and mass spectrometry interactomics) mapping host tissue perturbations, cellular entry factors, and downstream pathological cascades.

Table 2: Machine Learning and Computable Phenotyping Frameworks for Patient Stratification. This table details unsupervised, supervised, and natural language processing (NLP) machine learning architectures applied to large-scale electronic health records (EHRs) and clinical cohorts to establish biological endotypes.

cluster centers on anosmia, dysgeusia, and chronic pharyngitis (Halamka et al., 2020; Pfaff et al., 2023). The age-related comorbid cluster, concentrated in adults aged 65 years and over, is characterized by decompensation of heart failure, type 2 diabetes, and vascular disease (Pfaff et al., 2023).

4.3.2 Trajectory clustering and language models

Self-organizing maps project multidimensional clinical inputs—age, inflammatory markers, length of intensive care stay, oxygen requirement—onto two-dimensional grids (Ilbeigipour et al., 2022). Older age and elevated C-reactive protein, lactate dehydrogenase, and aspartate transaminase track with acute severity, yet the spread of symptom trajectories across all age bands implies that post-acute risk is not simply a function of demographics (Benito-León et al., 2021; Ilbeigipour et al., 2022). Deep-learning NLP of unstructured notes suggests that narrative concepts, such as transient anosmia or subtle dysautonomia, may predict later outcomes better than structured billing codes (Halamka et al., 2020). Population network modeling places these clinical features within a broader web of social and occupational determinants (Coste et al., 2025). Deep-learning architectures reviewed by Jamshidi et al. (2020) point to further possibilities, particularly for time-series and cardiac data, although their specific value in long COVID is yet to be tested (Table 2).

4.4 Network Algorithms and Repurposing Performance

4.4.1 Comparative performance of individual algorithms

Most repurposing pipelines began from the 332 viral–host interactors mapped by Gordon et al. (2020) and projected them onto a reference human interactome of roughly 18,500 proteins (Morselli Gysi et al., 2021) (Table 3; Figure 4). SAveRUNNER prioritized a few hundred off-label drugs, including histamine antagonists, corticosteroids, and sodium-channel modulators (Fiscon et al., 2021). Graph-kernel methods, using regularized Laplacian and commute-time kernels, reported an AUC-ROC of about 0.705 with favorable precision among the top-ranked candidates, outperforming simple shortest-path proximity (Santos et al., 2022). The GNN framework of Hsieh et al. (2021) identified 22 promising drugs, among them azithromycin, atorvastatin, aspirin, acetaminophen, and albuterol, and sought support from genetic, in vitro, and population-level EHR evidence covering more than 34,000 hospitalized patients. Li et al. (2021) narrowed 78 proximity-derived candidates to 30, seven of which had entered clinical trials by early 2021.

4.4.2 Multimodal consensus

The most persuasive result in this set, in our view, is the advantage of combining methods. By aggregating rankings from multiple pipelines, Morselli Gysi et al. (2021) reported an experimental hit rate of up to 62% in cell-based viral inhibition assays, compared with approximately 0.8% for unguided chemical screening (Table 3). Equally notable was their finding that more than 95% of active network-prioritized drugs did not bind viral proteins directly; they acted instead on host proteins near the disease module (Morselli Gysi et al., 2021). One should remember that these assays measured antiviral activity in acute infection models, not improvement in post-acute symptoms.

4.5 Combination Design and Crowdsourced Discovery

4.5.1 Complementary Exposure combinations

Screening with the Complementary Exposure rule—both drugs proximal to the module (z < 0), their target sets separated (s_AB > 0)—produced several candidate pairs (Zhou et al., 2020a) (Table 4; Figure 4B). Sirolimus with dactinomycin pairs mTOR inhibition with inhibition of RNA synthesis; mercaptopurine with melatonin combines a purine antimetabolite with an agent acting on circadian, neuroprotective, and anti-inflammatory pathways; and toremifene with emodin couples a selective estrogen receptor modulator with an anthraquinone (Zhou et al., 2020a). Independently, Hsieh et al. (2021) proposed etoposide with sirolimus and hydroxychloroquine with melatonin. Melatonin and sirolimus recur across both analyses, which is interesting, though recurrence across related methods is not the same as independent confirmation.

4.5.2 Knowledge graphs, traditional medicine, and patient reports

CovMulNet19 connects SARS-CoV-2 proteins, human interactors, diseases, symptoms, and compounds in one multilayer structure, allowing clinical manifestations to be traced to molecular neighborhoods (Verstraete et al., 2020). CoREx supports interactive exploration of drug perturbation and CMAP reversal scores (Santos et al., 2022). Data mining of traditional prescriptions highlighted Lianhua Qingwen capsules and Ma Xing Shi Gan decoction as network-coherent formulas with anti-inflammatory, antiviral, and neuroprotective annotations

Table 3: Computational Network Medicine Algorithms for Disease Module Discovery and Drug Repurposing. This table outlines computational platforms, topological metrics, and machine learning models used to quantify drug-disease module proximity and screen marketed pharmaceuticals for repurposing.

Table 4: Network Pharmacology, Synergistic Combination Models, and Crowdsourced Discovery Platforms. This table summarizes network-based multi-target combination models, multilayer multiplex knowledge graphs, interactive analytical explorers, and social media crowdsourcing pipelines.

(Sun et al., 2020). Mining of a long COVID Reddit community identified clusters of self-reported treatments, broadly comprising vitamins and supplements, neuropsychiatric agents, and antihistamine or anti-inflammatory medicines (Koss & Bohnet-Joschko, 2022). These signals are, at best, hypothesis-generating; they would need to be tested in the kind of living evidence synthesis now under way (Zeraatkar et al., 2025) (Table 4).

5. Toward a Network-Informed Taxonomy of Long COVID—Promise, Limits, and Next Steps

5.1 Principal Findings in Context

This review set out to ask whether network medicine could offer a more useful way of describing long COVID than a single diagnostic code. On balance, we think the answer is a cautious yes. Three lines of evidence, developed largely independently, point in a similar direction. Molecular studies suggest that SARS-CoV-2 perturbs a connected module of the human interactome and that downstream signals reach tissue-specific neighborhoods in lung, vasculature, gut, and brain (Gordon et al., 2020; Morselli Gysi et al., 2021; Pavel et al., 2021; Zhou et al., 2020b, 2021). Clinical data mining shows that people coded with long COVID fall into recognizable endotypes (Pfaff et al., 2023). And network pharmacology offers a principled, if still largely untested, way of matching drugs to perturbed sub-networks (Fiscon et al., 2021; Zhou et al., 2020a). The convergence is suggestive (Figure 1). Whether it is more than suggestive is a separate question.

5.2 Linking Molecular Modules to Clinical Endotypes

One of the more appealing features of the network view is that it lets us imagine explicit links between molecular modules and the clinical clusters identified in EHR data (Figures 2 and 3). The neurological and cognitive endotype described by Pfaff et al. (2023) fits reasonably well with the neurovascular and microglial signals reported by Zhou et al. (2021), in which endothelial and inflammatory mechanisms rather than neuronal infection seem to dominate. The gastrointestinal endotype, more common in younger patients, sits alongside evidence of enterocyte ACE2–TMPRSS2 co-expression and proximity to inflammatory bowel disease modules (Zhou et al., 2020b). The cardiopulmonary endotype could, in principle, reflect the pro-angiogenic and thrombo-inflammatory hubs identified by Pavel et al. (2021).

We want to be careful here, though. These correspondences are drawn across studies, populations, and data types; none of the included studies measured molecular modules and clinical endotypes in the same patients. The mapping is, for now, a hypothesis—a well-motivated one, perhaps, but a hypothesis all the same (Table 1; Table 2).

5.3 What the Repurposing Evidence Does and Does Not Show

The computational repurposing literature is impressive in scale, and the consensus results reported by Morselli Gysi et al. (2021) are genuinely encouraging. Yet almost all of these pipelines were designed to find antiviral or anti-inflammatory activity in acute infection (Table 3). It is not obvious that a drug able to suppress viral replication in a cell line will relieve post-exertional malaise or cognitive fatigue many months later. The finding that most active “network drugs” act on host neighbors rather than viral proteins is, we would argue, the more transferable lesson: if long COVID is sustained by host sub-network dysfunction, then host-directed modulation is where attention should go (Morselli Gysi et al., 2021; Zhou et al., 2021).

Combination strategies deserve similar nuance. The Complementary Exposure framework is mathematically elegant, but for long COVID the proposed pairs remain in silico (Hsieh et al., 2021; Zhou et al., 2020a) (Table 4; Figure 4). Several of the candidate agents—dactinomycin, mercaptopurine, etoposide—carry substantial toxicity, and their risk–benefit balance in a chronic, non-fatal condition would need very careful thought. Agents with gentler profiles that recur across analyses, melatonin being one example, may be more realistic first candidates for trials, though recurrence across related methods should not be mistaken for independent validation.

5.4 Methodological Considerations

Several limitations cut across the literature. The first is the acute-to-chronic gap already mentioned: much of the omic evidence was generated in acute or hospitalized COVID-19, and extrapolation to post-acute disease is inferential. The second concerns the interactome itself. Reference networks are incomplete and biased toward well-studied proteins, which can inflate the apparent centrality of familiar genes (Barabási et al., 2011; Conte et al., 2020). Results can shift depending on which interactome, distance metric, or null model is chosen (Santos et al., 2022; Stolfi et al., 2020).

Clinical phenotyping has its own vulnerabilities. EHR-derived clusters depend on coding practices, which vary between institutions and over time, and U09.9 was introduced only in late 2021, so earlier cases are under-captured (Pfaff et al., 2023). Unsupervised methods will always return clusters; whether those clusters are stable, reproducible in other health systems, and biologically meaningful has to be shown rather than assumed (Benito-León et al., 2021; Ilbeigipour et al., 2022). Crowdsourced data from social media are especially prone to selection and reporting biases, as Koss and Bohnet-Joschko (2022) acknowledged. Finally, our own review has limitations. It is narrative rather than meta-analytic, it is restricted to English-language sources, and the rapid pace of long COVID research means that relevant work will have appeared since our search.

5.5 Implications for Research and Practice

If an endotype-based taxonomy is to become useful at the bedside, a few things probably need to happen. Prospective cohorts should collect multi-omic samples—ideally including single-cell data from blood and, where feasible, cerebrospinal fluid—alongside structured and narrative clinical data from the same patients over time. That would allow the cross-study correspondences proposed in Section 5.2 to be tested directly. Computable phenotypes should be validated externally, across health systems and countries, and linked to patient-reported outcomes (Coste et al., 2025; Pfaff et al., 2023). Therapeutic candidates prioritized by network methods should then enter platform or adaptive trials stratified by endotype, and their results should feed into living evidence syntheses such as the network meta-analysis described by Zeraatkar et al. (2025). Open, well-documented tools—NeDRex, CovMulNet19, CoREx—lower the barrier for others to reproduce and extend this work (Sadegh et al., 2021; Santos et al., 2022; Verstraete et al., 2020).

For clinicians, the immediate message is more modest. It may already be helpful to think of a patient with long COVID as belonging, provisionally, to one of several patterns rather than to a single entity; this can shape which investigations are ordered and which specialists are involved. But network-predicted drugs should not, at this stage, be used outside trials.

5.6 Future Directions

Looking further ahead, integrating longitudinal omics with graph neural networks and knowledge graphs may eventually make it possible to assign individual patients to endotypes and to track whether they move between them over time (Hsieh et al., 2021; Santos et al., 2022). The same framework might also prove useful for other post-infectious syndromes, which have historically suffered from the same diagnostic vagueness. That prospect is, admittedly, speculative. It is nonetheless one of the stronger arguments for investing in the infrastructure now.

6. Conclusion

Long COVID is probably not one disease, and treating it as though it were may be part of why progress has been slow. The studies reviewed here, drawn from interactomics, multi-omics, machine learning, and network pharmacology, suggest that the condition reflects perturbations in several connected host sub-networks—neurovascular, thrombo-inflammatory, intestinal, and metabolic—that surface clinically as distinguishable endotypes. Network-based methods have already produced a structured list of repurposable drugs and rational combinations. Still, most of the molecular evidence comes from acute infection, and the links between modules and clinical clusters remain largely inferred. The next step, we suspect, is less about building new algorithms than about gathering better data from the right patients: prospective, multi-omic cohorts, externally validated phenotypes, and endotype-stratified randomized trials. If those pieces do come together, a network-informed taxonomy of this kind could make long COVID care more precise and offer a template for other post-viral conditions.

References