So it remains to answer the main question of the article, whether using the augmented pathways as controlled-access highways is a better way to incorporate pathway information than just using individual edges.
The performance of other methods was not as clearly served by the augmented pathways; capDSD improved functional flow in the noisier STRING setting, but not on BioGRID.
Similar improvements are seen for capDSD with unweighted majority vote and functional flow on STRING, though these are not the best performing methods overall, while performance of multi-way cut degrades with augmented pathways.
For a given target compound c ∈ C and a putative exogenous biosynthetic pathway ρ, an augmented model of the metabolic phenotype of the engineered strain was built from the reference model.
However, the main contribution of this article is to show that there is a way to capture the cohesiveness of known pathways by calculating DSD on an augmented network, and that this way of representing pathways results in better performance than just incorporating the pathway edges themselves for most, but not all of the function prediction methods we study.
However, we also create capDSD which creates an augmented graph that represents the signaling pathways coherently using new sets of nodes and edges.
To test if factors, other than connectivity measures in the two putative pathways could explain CTL variance, an augmented model was computed using demographic (age and gender) and educational variables (confidential data not shown here).
We find that diffusion state distance (DSD), our recent diffusion-based metric for measuring dissimilarity in PPI networks, has natural extensions that incorporate confidence, directions and can even express coherent pathways by calculating DSD on an augmented graph.
If there is an A ∞ -map ϵ A : A → K we will call A augmented.
Incorporating confidence and pathways into our diffusion-based distance metric DSD, we studied whether it was best to incorporate pathway information as edges or as controlled-access highways in an augmented graph.
TDS provided an augmented view of the extent of genetic diversity, the covariation among polymorphisms, the evolutionary pathways, and the boundaries of the mutational space explored by the viral swarm.
