CSE 527 Computational Biology     [ Home Schedule ] Handouts ] Project ]



Announcements
  • The poster session will be held on Dec 12.
  • The deadline for project midterm report extended to Nov 14.
  • In Q #3 in the Problem Set 1, let's assume that Sigmas are diagonal matrices.  This means that the off-diagonal elements of Sigma_1, ..., Sigma_k are all zero.
  • The deadline for project proposal extended to Oct 17.
  • The first meeting will be held on Monday Sep 24.
  • Please subscribe to the course mailing list.


Course Information 
  • Instructor:  Su-In Lee (CSE 536, office hours: Thu 10-11am or by appointment)
  • TA: Sonya Alexandrova (office hours: M 1:30-2:30pm @ CSE 220)
  • Lectures: MW 12:00-1:20pm @ CSE 403
  • Course mailing list: cse527@cs.washington.edu


Course Description


Biological sciences are becoming data-rich and information-intensive. Nowadays it became possible to obtain very detailed information about living organisms. For instance, we can obtain DNA sequence (3 billion-long string) information, expression (activity) levels of >20,000 genes, and various clinical measurements from humans. The growing availability of such information promises a better understanding of important questions (e.g. causes of diseases). However, the complexity of biological systems and the high-dimensionality of data with noise make it difficult to infer such mechanisms from data.

Machine learning (ML) techniques have become very useful tools for resolving important questions in biology by providing mathematical frameworks to analyze vast amount of biological information. Biology is also a fascinating application area of ML because it presents new sets of computational challenges that can ultimately advance ML. In this course, we will discuss ML/statistical techniques that have been applied to exciting problems in genetics, systems biology, sequence analysis and predictive medicine.

No background in biology is required.

The final grade is based on four homework assignments (10% for each), a final project (50%) and students’ attendance/participation (10%).

Prerequisites


Students are expected to have taken undergraduate-level machine learning or statistics courses, and have programming skills in MatLab, R, C++, JAVA, Perl, or Python.