ML for Expression

About the Team: The ML for expression team essentially uses unsupervised learning techniques in order to identify the most relevant cell types in sc-RNA neuronal data sets. By identifying relevant cell types we can identify which kinds of cells are being activated. In other words, given a behavior trained on a particular sample we can attempt to identify what cell types are related to that specific behavior; as they may be more prevalent in that "behaved" dataset than others. Doing so also informs biologists what methods they use output the highest biological insight.