There will be no class on Sept 14, the second lecture will be on Sept 19
Overview:
This graduate course will cover some exciting
algorithms that have been developed to analyze genomic and functional data,
including Genome comparison and assembly, gene prediction, localization
of regulatory elements in the genome, and analysis and comparison of
biological networks. While the emphasis of the
class will be on discrete algorithms, we occasionally will talk about
probabilistic models (such as HMMs), and the interplay
between discrete and probabilistic models. The course is intended for
computer science graduate students, and all of the required biology will be
explained in the class. Students in biological and related sciences
with a strong computational background are encouraged to participate.
Expected Background:
Students should be familiar with algorithms
(at least CSC 373 level), basic probability theory.
Grading:
The basic requirements for the class will be a course project (40% of the
grade), three homework assignments (45% total), and reading/class
participation (15%).
Administrative details:
The class will satisfy the 2c breadth.