SPOKE Nodes and Edges

SPOKE (Scalable Precision Medicine Knowledge Engine) is a very large network containing multiple types of biological data. Pooling such diverse data into a single knowledge environment allows identifying new connections, with implications for biomedical applications like personalized medicine: suggesting which drugs may be effective for a specific patient. An earlier version of the network was used to suggest new uses for existing drugs (Himmelstein et al., 2017).

SPOKE is a heterogeneous network, meaning that different nodes (points) within the network can represent different types of data. The edges between pairs of nodes represent known connections. Paths that follow a series of edges may connect nodes not previously known to be related.

Node and edge types and their data sources are given below. Except as noted, data are updated weekly on a rotating basis (different types on different days).
   = updated currently    = in progress

See also: source licenses

Node Types

  • PharmacologicClass – from DrugCentral, the following annotation types:
  • Protein – human proteins in UniProtKB
  • Reaction – reactions in microbiome pathways
  • SARSCov2 – SARS-Cov-2 proteins studied in Gordon et al., 2020
  • SideEffect – all entries in SIDER
  • Symptom – terms in Medical Subject Headings (MeSH) subtree C23.888 (Diseases / Pathological Conditions, Signs and Symptoms / Signs and Symptoms)

    Edge Types

    Technical Notes

    MeSH term co-occurrence. The following were calculated for each pair of Medical Subject Headings (MeSH) terms:

    Disease-localizes-Anatomy and Disease-presents-Symptom calculations used only papers in which the disease term was major and the other term (anatomy or symptom) was used directly (no “explosion” via the MeSH tree). Disease and anatomy MeSH terms were taken from Disease Ontology and Uberon, respectively.


    A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Gordon DE, Jang GM, ... Shoichet BK, Krogan NJ. Nature. 2020 Apr 30.
    Systematic integration of biomedical knowledge prioritizes drugs for repurposing. Himmelstein DS, Lizee A, Hessler C, Brueggeman L, Chen SL, Hadley D, Green A, Khankhanian P, Baranzini SE. eLife. 2017 Sep 22;6. pii: e26726.