Computational Tasks
Networks can be combined with -omics data to achieve a wide range of
computational tasks. Below we define some broad categories that describe
these computational tasks. These categories are not mutually exclusive,
as many computational methods have the capacity to perform multiple
tasks or hybrids of them. For example, methods which “upscale
networks”, meaning they output a higher-level network from a
lower-level PKN, typically do both network inference and explanation
extraction, as they select a small subset of the input PKN that can
explain the correlations in the data and then will modify it to infer a
new, higher-level network. It is also common to use explanation
extraction or network inference task as a precursor to phenotype
prediction, especially in clinical applications.
Explanation extraction aims to interpret patterns found within an
omics profile and contextualize them using prior information about the
system. It addresses hypotheses around system changes, such as
differential expression or altered interaction strengths, to elucidate
the mechanisms involved21. Common examples of
explanation extraction tasks include enrichment-analysis and algorithms
that produce a relevant subgraph of a larger network. Explanation
extraction can also be thought of as emulating the literature search of
a molecular biologist to explain the data at hand. As a molecular
biologist reads the literature they ask “Is this information fragment
compatible with my data? Does it explain it or contradict it? Is this
applicable to my experiment’s context?”. The same questions are
interrogated by explanation extraction methods, but in a quantitative
manner that scales to high throughput data. Explanation extraction tools
generate valuable conjectures that can, for example, guide the selection
of subsequent perturbing agents, or recognize parallel mechanisms that
unify multiple datasets in a novel way12,19.
Network inference tasks produce a network model based on the
input -omics data. This can be achieved by integrating prior networks or
can be done de novo . Due to the combinatorial complexity of the
model space and the inherent stochasticity of biological systems,
inference is always an underdetermined problem and coherence of inferred
networks and actual biological reality may be low, independent of the
performance of the model. Constraining inference to at least partially
conform with known biology can help by “anchoring” inferred networks.
Another option is to use a large number of biological models in an
ensemble learning strategy to reduce bias.
Some network inference approaches construct an entirely new model while
others expand on established networks, in either case, the goal is to
generate new mechanistic hypotheses. Upscaling algorithms are a common
example of network inference. These approaches infer a higher-level
representation (e.g. Activity Flow) from a lower-level prior network
(e.g. protein-protein interactions) using -omics profiles. Upscaling can
also be used to assign weights, direction, sign and rate constants to
edges on a graph.
Phenotype prediction aims to predict how an organism or system
responds to disease states and perturbations. These methods may be
applied at a cellular level to project signaling events and
transformations as well as broad phenomena like cell proliferation and
survival, but they can also be extended to a network medicine approach,
where predictions are made at a patient level to inform diagnosis,
prognosis, or treatment response22,23.
Effective phenotype prediction is arguably more difficult than the prior
two tasks. Phenotype is a function of the whole system that often
contains feedback loops and other non-linear response circuitry. It is
also inherently multimodal as at minimum, it requires one omic
measurement and one phenotype measurement modality –e.g. IC50, GR50 or
disease free survival. Each of these factors can be confounding to
phenotype prediction tools.