TODOs:

* To find interesting pairs of genes:
  - Look for anti-correlated features used in a model
  - Look for features that appear together in trees more than expected.
* test usage of laplace loss function
* define function to annotate model features (currently done as part of plotting)
* switch to CV using OOB of multiple bootstrapped samples? (however CV is easier than BS to understand)
* cluster features
* add text describibg model parameters (n.trees, int.depth, shrinkage)
* generate table with all value presented in graph

- add training model AUC p-val
- print training model p-val in plot
- predict all CCLE lines
- add lineage barplot in plots
- plot pred features of the same gene of the target feature.
- look for features who don't correlate well with target overall across all models (so should have low Pearson correlation but high relative importance)
- test whether better to color all bars in barplots (by value)



CCLE fixes needed:
- names for Expr$cls
- all mutation features are available for the same number of lines (no?)