Application of supervised Machine Learning algorithms to RNASeq-Data (before/after treatment)

Question

[CG_90]

Hello everyone! I am planning on analysing RNASeq Data from three groups ("status"):

1. Cancer-free
2. Cancer Group 1
3. Cancer Group 2

I have obtained information on baseline expression (before treatment) and expression after Treatment (all three groups have been treated with 2 different treatments T1 and T2 ("dose")). I tried using MLSEQ for R, but their Pipeline uses raw count data, whereas I would like to analyse for changes in expression (see below) after treatment first and use that information to teach a to be determined supervised learning algorithm.

Is there any way to generate indivual fold changes while still using a rather complex design modell (I was thinking about using DESeq2, having a design in something of the likes of "design~dose+status+status:dose)?

Thank you all in advance!