Deep Learning Applied to Biology

I wanted to quickly take a moment to reflect on the work that our team has completed so far. We are in the midst of exciting state of the art research that fits at the intersection of deep learning and biological research.

A few areas that we are currently involved in:
• Optimal experimental design
• Novel deep learning architectures for gene expression
• Transfer learning from E. coli to Salmonella
• Natural Language Processing and graph inference for antibiotic resistance

Our multidisciplinary lab includes experimentalists, engineers, and computer scientists, along with welcoming personalities that makes it easy to learn and contribute at an exciting pace.

However, our progress in research has made me wonder… How fast is this field growing as a whole? That is, can we quantifiably put a number to the overall research growth of deep learning applied to biological problems?

I decided to perform a simple, yet interesting, experiment to provide us this number. I performed two different searches over a few different time slots on Google Scholar. One with the key word “deep learning” and the other containing the key words “deep learning” AND “biology”. I found it fascinating that there is exponential growth in the number of citations throughout the years, with the keyword “biology” being mentioned in almost 20% of them.

Clearly this field will continue to grow as more data, processing power, and methods continue to improve, and I am certainly excited to be able to witness the advances in medicine and deep learning that will result from it.

Great work everyone!