Abstract: Markov models and neural networks for fast, accurate, and interpretable sequence annotation
Characterization of genomic and protein sequences depends on fast, accurate, and interpretable methods for classification. In this talk, I will introduce the concept of sequence annotation, and will discuss the probabilistic models and sequence alignment algorithms often used as the basis of annotation. I'll supplement this with a description of neural network approaches that are primed to improve on state of the art methods, and discuss ways that we are integrating these two usually-disconnected approaches to sequence classification.