  Statistical and computational methods are widely used in today's scientific
studies. Using a female fertility potential in childhood cancer survivors as an
example, we illustrate how these methods can be used to extract insight
regarding biological processes from noisy observational data in order to inform
decision making. We start by contextualizing the computational methods with the
working example: the modelling of acute ovarian failure risk in female
childhood cancer survivors to quantify the risk of permanent ovarian failure
due to exposure to lifesaving but nonetheless toxic cancer treatments. This is
followed by a description of the general framework of classification problems.
We provide an overview of the modelling algorithms employed in our example,
including one classic model (logistic regression) and two popular modern
learning methods (random forest and support vector machines). Using the working
example, we show the general steps of data preparation for modelling, variable
selection steps for the classic model, and how model performance might be
improved utilizing visualization tools. We end with a note on the importance of
model evaluation.
