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Network medicine: a network-based approach to human disease
Open accessAbstract
Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.
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“Few genes act in isolation but rather affect the function of other genes to influence a particular phenotype via in cellular networks or pathways (Barabasi et al., 2011; Geschwind and Konopka, 2009).”
From Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders (2019) · cites this paper as Barabasi et al., 2011
In the citing paper
“Communities of co-expressed proteins can be linked to disease processes, and the most strongly correlated proteins, or “hubs,” within these co-expression modules are enriched in key drivers of disease pathogenesis5–10.”
In the citing paper
“Machine learning has been used to analyze high-throughput molecular interactomes to unravel genetic architecture perturbed in disease12,13 and help design therapies to target them14.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [14]
In the citing paper
“This evaluation method aligns with the approach predominantly used in the literature12,14–16,18–20.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [14]
In the citing paper
“TxGNN leverages an additional metric learning component for zero-shot prediction, capitalizing on the insight that diseases can share disease-associated genetic and genomic networks10,14 and, thus, medical knowledge of disease-associated networks can be transferred by the model from well-annotated diseases to other diseases to enhance predictions on diseases with limited treatment options (Fig. 2a”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [14]
In the citing paper
“In protein interaction networks, for instance, mutations in interacting proteins often lead to similar diseases [13].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“Further, essential genes are often found in hubs of a molecular network whereas non-essential genes (e.g., those associated with disease) are located on the periphery [13].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“Given by the shared components and disease module hypotheses [13], cellular components associated with the same phenotype tend to cluster in the same network neighborhood [16].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“Thus, the network parsimony principle dictates that shortest molecular paths between known disease-associated components tend to correlate with causal molecular pathways [13].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“First, interacting entities are typically more similar than non-interacting entities, as implicated by the local hypothesis [13].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“The organizing principles governing networks link network structure to molecular phenotypes, biological roles, disease, and health, thus providing the conceptual grounding that, we argue, can explain the successes (and limitations) of graph representation learning and inform future development of the field.”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“Because such processes are costly and time-consuming, in silico approaches have been adopted into the drug discovery pipeline.”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“Modern drug discovery requires elucidating a candidate drug’s chemical structure, identifying its drug targets, quantifying its efficacy and toxicity, and detecting its potential side effects [13, 14, 32, 164].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
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Metadata
- Authors
- Barabási, A.-L., Gulbahce, N., & Loscalzo, J.
- Affiliations
- Northeastern University (US)
- Brigham and Women's Hospital (US)
- Venue
- Nature Reviews Genetics
- Published
- 2010-12-17
- Volume
- 12
- Issue
- 1
- Pages
- 56-68
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC3140052
- OpenAlex
- W2062533676
- Cited by
- 5,034
- References
- 140
- Citations by year
- 2026: 421 · 2025: 322 · 2024: 296 · 2023: 299 · 2022: 346 · 2021: 390 · 2020: 384 · 2019: 356 · 2018: 383 · 2017: 326 · 2016: 311 · 2015: 324 · 2014: 309 · 2013: 272 · 2012: 217
- FWCI
- 15.79
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (HL81587, HL 70819, U54HL070819, P01HL081587, R21HL089734, R01HL061795, HL-48743, P01HL048743, HL-061795, R37HL061795)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- network medicine, molecular networks, disease-associated genes, disease-associated mutations, therapeutic target, biomarkers, disease modules, complex diseases, genome-wide association study, full genome sequencing
- MeSH terms
- Disease, Humans, Gene Expression Profiling, Databases, Genetic, Gene Regulatory Networks, Metabolic Networks and Pathways, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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