Week 3 – Implementing Bayesian Weighting Schemes

The achievements from last week as well as the goals were laid out in this progress report. Accordingly, I spent the majority of this week working on an implementation of the Bayesian Weighting Scheme I discussed last week. This involved carefully fleshing out all the minutiae of the relevant paper (which I laid out and explained in this document from last week). I then applied the code onto a small toy-network that I made myself and then hand-calculated a few edges to verify my implementation.

Toy Network from Bayesian Weighting Scheme Implementation

I am currently working to implement the weighting scheme onto the HIPPIE Interactome. Thus far this has meant simply writing code to read the relevant text files and format the data efficiently in a way that meshes well with code I wrote for the toy example. Eventually, I will compare the scores of edges from this weighting scheme to the ones on the actual HIPPIE Interactome and further analysis on the differences between the two weighting schemes will probably be among the next steps. Besides implementing the Bayesian Weighting Scheme I have also been working to understand the underlying statistics behind the methods used to weight the HIPPIE Interactome. I will try to have a document that explains the mathematics for this in similar fashion to the one for the Bayesian Weighting Scheme.