Welcome!

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 our coverage of recent research literature in 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.

When you complete this course, you will be able to:

• Connect different state-of-the-art models like Convolutional Neural Networks, Recurrent Neural Networks, etc. to applications in genomics
• Extract key ideas from research papers when solving homework assignments
• Think critically about using a deep-learning method for a new task - what works, what doesn’t work, and how a particular model may or may not be appropriate for the task.
• Collaborate with classmates on a team project to apply deep learning models to a genomics task
• Communicate your findings (both positive and negative results are encouraged) clearly by writing a research paper and through oral presentations.

All classes will be conducted online over zoom and recorded for future viewing. Active student participation during the class is highly encouraged. Students anticipating difficulties in attending classes at the assigned time are recommended to email the instructor by January 19, 2021, so that accommodations could be made accordingly. We will be using the following websites for the smooth running of the online course:

• Course announcements will be made on Piazza.
• Assignments, lecture materials, and any extra resources will be uploaded on the course website.
• Assignments will be submitted and graded on Gradescope.
• Classes will be conducted via Canvas Zoom interface that will allow the recordings to be available immediately after class.

Professor: Ritambhara Singh (ritambhara_singh@brown.edu)
Time & Location: TTh 10:30am – 11:50am EST through Zoom (link in Canvas)

Midterm/Final Group Preference Form (due 2/9): https://forms.gle/hguiVKMPeoNF7JW87
Documents: Course Missive
Canvas: https://canvas.brown.edu/courses/1084361
Piazza: https://piazza.com/brown/spring2021/csci1850
Gradescope: https://www.gradescope.com/courses/228828
Contact TAs only: cs1850tas@lists.brown.edu
Contact HTA + Professor: cs1850headtas@lists.brown.edu