I am looking for advice on how to best approach a problem I am faced with.
I have a dataset of numerous degradative biomarkers, clinical information and various other measures (from clinical trials). I would like to see how different biomarkers relate to each other, and effect their levels (high/low etc).
My goal is to gain an understanding of how these biomarker levels fluctuate in different disease profiles - and understand if there is a relationship between each of them (co/independent). The limitation is that I do not have access to genetic or environmental data so this will be a "random effect". There are around 1000 marker samples and 10's of biomarkers mixed between clinical and biochemical. There are also numerous time points to be incorporated.
I have thought about implementing something similar to a protein-protein interaction network, although this will of course not involve physical interactions, but more pathway interactions for further hypothesis driven research.
I wanted to firstly: validate if this project actually makes sense and secondly: ask for some advice around how to best implement this.
I have experience implementing neural/deep nets, directed and undirected networks (Bayes) in python, but not sure exactly how to go about this.