Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery
Fig 1
Schematic representation of the RPath algorithm.
Step 1) All acyclic paths of a given length between a drug and a disease in the KG are calculated. If there exist causal acyclic paths connecting the drug and the disease, a subgraph involving all these paths is inferred. This subgraph represents the proposed mechanism of action by which the drug may be a therapeutic target of the given disease. Step 2) Transcriptomic signatures observed from a drug-perturbed experiment are overlaid onto each corresponding node present in these paths. Then, RPath traverses through each path and evaluates whether the inferred direction of regulation (i.e., activation or inhibition) at every step is concordant with the up- and down- regulations (i.e., red and green nodes, respectively) observed in the transcriptomic signatures. Step 3) In a similar manner, transcriptomic signatures observed within a specific disease context are overlaid onto each corresponding node in the concordant paths from the previous step (if any). Next, RPath evaluates whether the disease transcriptomic signatures contradict the paths that were concordant with the drug signatures. If this is the case, the specific drug-disease pair is prioritized.