CSCI2952G Fall 2020: Deep Learning in Genomics

Location: CIT 101 (Remote access)
Meeting times: TTh 1PM-2:20PM
Instructor: Ritambhara Singh
E-mail: ritambhara@brown.edu
Office hours: TTh 2:30PM-3:30PM
Office Location: CCMB Room 313 (164 Angell St.)

Can deep learning models that have defeated gamers or recognized images better than humans also help us understand genomics? How far will this interdisciplinary research take us on our quest to cure cancer? In an era with faster-than-Moore’s-Law exponential growth of the genomics data (Berger et al. 2016), deep learning methods are finally able to assist in solving essential problems in the field. However, these exciting developments also face challenges that are unique to working with data from our DNA.
As researchers trying to combine deep learning and genomics, we have to think carefully about applying these models effectively to genomics tasks. Is it appropriate to use deep learning for our application? What model should we use? Will our approach improve our understanding of the data or the problem? In this course, you will answer these questions by reading recent research literature and discussing it during the class. You will learn about different genomics tasks, deep learning models, and how they fit together. The course is designed to enable critical thinking and allows students to work together to apply these models.

Online course format

All classes will be in-person with the option for remote access. Active in-person student participation during the class is highly encouraged. Students anticipating difficulties in attending classes in-person are recommended to email the instructor by September 01, 2021, so that accommodations could be made accordingly. Please refer to the Course Missive below for the details.

Course Materials

Presentation schedule

Course Missive (to be updated)