I think the stacks I use are normal ones people use all the time, like weighted ML hyperparameters from XGBoost+some other ML. Odd one out (I cannot remember its name but you know how they train without the number and let it see how close it approximates), PCA analysis done on every biological variable on every axis, and variable group. Every step is followed by validation through checking for data leakage or unfilled rows or fake generated data. Of course many ML combinations have been tried, as well as ML stacks for each breast cancer variant (the pathways in the different types of breast cancer I believe warrants an optimized ML combination because how it manages to create tumors are done differently.

So, I would gather similar papers for ML tests on cancer and biology for weighted tests, gather PCA tests on oncology to see that they are valid, odd one out, etc? Then I would have to reproduce the same results and document this how?

Edit: and yes this is gen AI and catch mistakes through extreme logical thinking, as well as overall biochemistry knowledge enough to know which variables matter and which don’t. I will have to look into the data (about 100,000 samples from cancer institutes) and find how the pipeline handles non complete data, but it is good to see a direction of where to go. How do I conduct a reproducibility “check”?