Evaluate performance of covariates on TP53

Creates an explore directory and README for this type of exploratory notebook.

See how well covariates (non-expression features) predict TP53 mutation.

Related to cognoma#8:
General mutation-load does provide some ability to predict mutation status of
TP53.

Partially addresses cognoma#21:
Covariates are extracted from samples.tsv.

explore/README.md

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+# A directory for exploratory machine learning analyses
+
+This directory is home is exploratory analyses that help answer questions about how we should do machine learning. For algorithm implementations see the [`algorithms`](../algorithms) directory. For other types of analyses, place them here.
+
+Notebooks should be exported to scripts for review. For example, from the directory containing your scripts run:
+
+```sh
+jupyter nbconvert --to=script *.ipynb
+```

explore/confounding/README.md

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+This analysis looks into covariates and their potential confounding effects.
+
+Specifically, we find that disease type, gender, and mutation burden predict _TP53_ mutation with AUROC = 84%.