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Cited by 3,242Bioinformatics and Genomic NetworksBiomedical Text Mining and OntologiesComplex Network Analysis Techniques

The human disease network

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Goh, Kwang-Il, Cusick, Michael E., Valle, David, Childs, Barton, Vidal, Marc, Barabási, Albert-László·2007·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.0701361104PMC PMC1885563
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Abstract

A network of disorders and disease genes linked by known disorder–gene associations offers a platform to explore in a single graph-theoretic framework all known phenotype and disease gene associations, indicating the common genetic origin of many diseases. Genes associated with similar disorders show both higher likelihood of physical interactions between their products and higher expression profiling similarity for their transcripts, supporting the existence of distinct disease-specific functional modules. We find that essential human genes are likely to encode hub proteins and are expressed widely in most tissues. This suggests that disease genes also would play a central role in the human interactome. In contrast, we find that the vast majority of disease genes are nonessential and show no tendency to encode hub proteins, and their expression pattern indicates that they are localized in the functional periphery of the network. A selection-based model explains the observed difference between essential and disease genes and also suggests that diseases caused by somatic mutations should not be peripheral, a prediction we confirm for cancer genes.

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  • Gene expression and regulation can be highly tissue-specific.

    “Gene expression and regulation can be highly tissue-specific, and most disease-related genes have tissue-specific expression abnormalities [16, 17].”

    From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [16]

    The Results report that nonessential disease genes tend to be expressed in only a few tissues and that genes associated with the same disorder tend to be expressed together in specific tissues. This supports tissue-specific expression, but the paper analyzes expression in healthy tissues; it does not establish that most disease-related genes have tissue-specific expression abnormalities or address tissue-specific regulation. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Most disease-related genes have tissue-specific expression abnormalities.

    “Gene expression and regulation can be highly tissue-specific, and most disease-related genes have tissue-specific expression abnormalities [16, 17].”

    From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [16]

    The Results report that genes associated with the same disorder tend to be expressed together in specific tissues, and that nonessential disease genes tend to be expressed in relatively few tissues. Those findings concern expression patterns measured in healthy tissues, not tissue-specific expression abnormalities; the reported 68% also refers to disorders, not to the proportion of disease-related genes. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Machine learning has been used to analyze high-throughput molecular interactomes to unravel genetic architecture perturbed in disease and help design therapies to target them.

    “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 [13]

  • 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 [12]

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Cites

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Authors
Goh, K.-I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A.-L.
Affiliations
  • University of Notre Dame (US)
  • Harvard University (US)
  • Johns Hopkins University (US)
  • *Center for Complex Network Research and Department of Physics, University of Notre Dame, Notre Dame, IN 46556;
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2007-05-22
Volume
104
Issue
21
Pages
8685-8690
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.0701361104
PMC
PMC1885563
OpenAlex
W2083045667
Cited by
3,242
References
2
Citations by year
2026: 70 · 2025: 84 · 2024: 110 · 2023: 114 · 2022: 144 · 2021: 203 · 2020: 183 · 2019: 199 · 2018: 194 · 2017: 193 · 2016: 224 · 2015: 240 · 2014: 221 · 2013: 228 · 2012: 215
FWCI
55.06
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Science Foundation (0513650, 0926737)
  • W. M. Keck Foundation
  • Dana-Farber Cancer Institute
  • National Institutes of Health
  • U.S. Public Health Service (IH U01 A1070499-01)
  • National Human Genome Research Institute
  • National Cancer Institute (U56CA113004)
  • National Institute of General Medical Sciences
  • Division of Materials Research
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
gene-disease networks, gene-disease association, hub proteins, essential genes, gene essentiality, protein-protein interaction, gene expression profiling, functional modules, cancer genes, somatic mutations, human interactome
MeSH terms
Computer Simulation, Disease, Gene Expression Regulation, Humans, Genetic Predisposition to Disease

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