In the context of networks, heat diffusion refers to adding a scalar value to each node (i.e., heat) and simulating how heat can flow across edges to neighboring nodes, and so on. In networks biology, this allows for the assessment of connectivity and topology of a network. With directed, causal networks, such as those encoded in BEL, heat diffusion can allow data-driven identification of relevant/dysregulated pathways and subgraphs.
Since networks may contain contradictions like
A increases B and also
increases A that cannot be easily explained by their associated biological context
annotations, a network is derived that deletes one of them. For a network with only one
contradiction, this only requires deriving two networks to enumerate all possibilities. As more
contradictions arise, there are exponentially many possible derived networks. A randomized
approach is used to sample from these networks and generate scores for each. This can be used to
calculate an average score and also assess the sensitivity of that score.
Data sets can be directly uploaded and analyzed. The results of these experiments can then be directly overlaid to the interactive network viewer to provide a data-driven analysis of given networks or sub-networks.
Heat diffusion is a general enough methodology that it can be applied to a variety of networks and different types of data sets. For example, Leiserson et al. used undirected protein-protein interaction networks with copy number variations as heat to identify functional modules in the context of cancer.
Other data types, like single-nucleotide polymorphisms (SNPs), clinical measurements, or neuroimaging features, which have been annotated to our Alzheimer Disease Knowledge assembly with NeuroMMSig.
BEL Commons is developed and maintained in an academic capacity by Charles Tapley Hoyt and Daniel Domingo-Fernández at the Fraunhofer SCAI Department of Bioinformatics with support from the IMI project, AETIONOMY. It is built on top of the open source project, PyBEL. Please feel free to contact us here to give us feedback or report any issues.