I have studied bioinformatics and as such I have a particular idea of the domain of medical statistics and the domain of bioinformatics.

Big data often means that testing for 5% significance is a bad idea. As a result people working on big biological data weren't very welcome by the frequentists in medical statistics and bioinformatics formed it's own community.

That community split produces effects such as bioinformatics having it's own server for R packages and not using the server in which the statistics folks put their R packages.

In another post in this thread bokov speaks of wanting to use Hidden Markov Models (HMM) for modeling. HMM is the classic thing that based on Bayes rule and that people in bioinformatics use a lot but that's not really taught in statistics.

Understanding the 'complicated' maths or Bayes theorem

Understanding Bayes theorem is not hard. Bayes theorem itself is trivial to learn. Understanding some complex algorithm for determining Hidden Markov Models based on Bayes rule is the harder part.

Machine Learning is also a different community then standard statistics. It's also not only about Bayes theorem. There are machine learning algorithms that don't use Bayes. Those algorithms are still different than what people usually do in statistics.