Université du Québec à Montréal, Montreal, Quebec, Canada
November 18-21, 2017
Machine learning has been applied to an almost magical effect in language and image processing for business, and yet breakthroughs in biological image processing or biological sequence analysis have come much more slowly. Is this because scientists aren’t interested, do they lack the knowledge or skills, or is it because biological problems present obstacles that we don’t see in other applications? It may be that it is a combination of all three, and as these obstacles are removed, we will see a renaissance in biological breakthroughs using AI. This talk will be 1/3 background, and 2/3 practical applications as we walk through some simple code examples using Sci-kit learn and Keras to show how anyone can propose and test hypotheses using publicly available biological data sets
Shelly DeForte is a Post-Doctoral researcher at the Université de Montréal in the Biochemistry department. She studies the relationship between protein sequences and their physical behavior in the cell using data wrangling and machine learning tools in Python.