Integrative analysis of omics studies using machine learning I would like to use public omics datasets (ChIP-seq, RNA-seq, and ATAC-seq) from different studies to do an integrative analysis as follow:


Normalise samples, within each type of omics, from different public datasets.
Convert the normalised values into a uniform scale to make the comparison between ChIP-seq, RNA-seq and ATAC-seq possible.
Feed the normalised uniformed values into machine learning to infer one feature (e.g. RNA expression) from other features (e.g. TF or histone marks ChIP-seq).


How could I integrate these data-sets to predict one group of variables from another one?
The steps you describe are correct. For step 2 it is usually normalized to mean 0 and variance 1. However the "machine learning" part is important. 

Having several samples being technical replicates will make the integration task easier. However, you have too few samples to make any good prediction. At most I would describe it as an exploratory analysis.

I recommend using regularized canonical correlation analysis for this kind of multi-omics analysis. The method seeks which are the features of one dataset that correlate (or covariate, depending on a parameter) with the variables of other datasets. Then you could use those features to predict the position of the other. 

There are several implementations of this methods, the original in the RGCCA package, and mixOmics for other procedures with cross-validations also STATegRa has other related methods.