Network biology concepts in complex disease comorbidities
Abstract
The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge that can facilitate drug repurposing and the development of targeted therapeutic strategies.
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Joint modeling of patient phenotypic features is essential for identifying genetic mutations with pleiotropic effects.
“This joint modeling of patient phenotypic features is essential for identifying genetic mutations with pleiotropic effects74–76.”
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GJH is estimated to affect approximately 10-20% of the general population.
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Network organizing principles connect network structure to molecular phenotypes, biological roles, disease, and health.
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From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [14]
Modern drug discovery requires elucidating a candidate drug's chemical structure, identifying its targets, quantifying efficacy and toxicity, and detecting side effects.
“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 [14]
In silico approaches have been adopted into the drug discovery pipeline because drug discovery processes are costly and time-consuming.
“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 [14]
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- Authors
- Hu, J. X., Thomas, C. E., & Brunak, S.
- Affiliations
- University of Copenhagen (DK)
- Corresponding author
- Brunak, Søren
- Venue
- Nature Reviews Genetics
- Published
- 2016-08-08
- Volume
- 17
- Issue
- 10
- Pages
- 615-629
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- OpenAlex
- W2493614923
- Cited by
- 328
- References
- 174
- Citations by year
- 2026: 11 · 2025: 28 · 2024: 22 · 2023: 32 · 2022: 40 · 2021: 37 · 2020: 38 · 2019: 44 · 2018: 40 · 2017: 31 · 2016: 4 · 2015: 1
- FWCI
- 3.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- Funders
- Novo Nordisk Fonden
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- dynamic network model, pleiotropy, network robustness, network rewiring, disease co-occurrence, external exposure, electronic health records, drug repurposing, targeted therapy
- MeSH terms
- Disease, Humans, Comorbidity, Computational Biology, Systems Biology, Metabolic Networks and Pathways
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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.
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