Welcome to corneto Discussions!

[pablormier]

🍦 Welcome to CORNETO Discussions!

We're using GitHub Discussions to connect with the community around CORNETO — a unified framework for network inference from omics data and prior knowledge.

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[MV511]

Hi Pablo, Mattia from the DAL Lab here!
Got a question that maybe is cool to share on Git (might be a naive one).
I'm trying to use CORNETO with an inverse CARNIVAL flavor, which I noticed is not implemented straightforward. Right now, I'm doing what I used to in CARNIVAL, thus I'm adding an artificial Perturbation node connected to all source nodes of the OmniPath subnetwork I provide as PKN to let the solver sort it out (I'm using CPLEX).
Is that the right way to do it? I later launch the method in this way:

P = create_flow_carnival_v4(Gf, exp_list,
lambd=args.penalty,
penalty_on="signal",
set_perturbation_values=False,
fix_input_values=True) #now the artificial Perturbation node is active. Theoretically, all the Perturbation-to-original-source edges will be treated as part of the penalized solution
return P, Gf, exp_list

Personally, I think this is probably not how you would run an inverse CARNIVAL problem in CORNETO, so I was wondering if you have hints on that. I think in this way, I'm adding too much penalty due to the multiple input sourcing (if using a reasonable lambda) and this leads the solver, after hours, to decide that an empty network is better than introducing any input.
If you have suggestions I'm open to them.
Thanks,

Mattia