That being said, even today, a lot of these traditional concepts are very much applied to build useful predictive models from vast sets of experimental data. But what really changed was the introduction of deep learning, along with (a) access to newer information and technology, (b) an exponential decrease in computing costs, (c) an exponential decrease in cost for genome sequencing, (d) advances in lab instrumentation and (e) a generation of trained scientists who understand the complexities of biology and biological systems and also have the ability to go deep into computer science.
Where is deep learning making inroads?
Life science research is vast and it is almost impossible to provide a comprehensive answer to this question. A lot of interesting work, ranging from biomedicine to understanding gene regulation have been published in the last few years. To me, one of the more interesting areas of application is the drug discovery space, such as predicting molecule toxicity and reactivity, which is often a huge burden on the drug discovery pipeline or even drug repurposing.
In my personal opinion, it is the best time to be a computational biologist, as we have access to innumerable amount of resources and information; and biology is filled with unanswered questions.