Cited by 328Bioinformatics and Genomic NetworksMental Health Research TopicsGene Regulatory Network Analysis

Network biology concepts in complex disease comorbidities

Hu, Jessica Xin, Thomas, Cecilia Engel, Brunak, Søren·2016·Nature Reviews Genetics

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.

Integrity signals

Registry

Crossref · 10.1038/nrg.2016.87

Cites

174 of 174 references verified0 citation errors · 0 not checkable

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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.”
  • GJH is estimated to affect approximately 10-20% of the general population.

    “GJH is estimated to affect approximately 10–20% of the general population. 4”
  • Network organizing principles connect network structure to molecular phenotypes, biological roles, disease, and health.

    “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 [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
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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