Predicting the function of unkown transcription factors in P. aeruginosa using supervised learning
In this blog post I’m going to continue the pipeline of my article Functional prediction of hypothetical transcription factors of E.coli K-12. However, I’m going to add a little twist to the former, with a little of network science and supervised learning.
The overarching question we’re asking is: can we leverage different sources of information about an organism, to predict the function of its genes with unkown function?
Pseudomonas aeruginosa is a pathogen and can cause the deadly disease cystic fibrosis in humans. Thus understanding the function of its transcription factors can lead to novel therapeutics.
You can read through the code in this link.