9.6 years ago by
Machine learning has certainly found wide application in bioinformatics. A trivial example: search PubMed for the phrase 'support vector machine'. There are currently 2262 results applied to diverse problems such as predicting protein-protein interaction, identifying features in nucleic acid sequences, analysis of microscopy images and the physiology of muscles during exercise.
A word of caution regarding "big, unsolved problems." Bioinformatics is not really like this: we are not looking for the Higgs boson, or figuring out what came before the big bang. The vast majority of day-to-day bioinformatics consists simply of helping biologists to get more from their data. Very often, this requires nothing more than the intelligent application of existing tools from mathematics, statistics and computer science. You should realise that for many biologists, the automation of a manual procedure taking many hours using a simple shell script that takes milliseconds is an absolute revelation.
The key thing then, when entering the field, is to figure out the problems faced by biologists, not the theoretical problems that take your fancy. And the best way to do that is talk to them. That way, you can identify those areas where you can best apply your expertise and bring something new and useful to the table.