Course Announcement for Spring 2008
A new skill combining biology and computing has exploded into what is probably the hottest career opportunity for college graduates in the coming decade. Government and private research laboratories, including all the major drug makers, are desparately scouring universities for people trained in computational biology, also known as bioinformatics. ... Because of the demand, salaries for newly minted Ph.D.s competent in both biology and computer science average $90,000 a year.
- Robert Boyd, Knight-Ridder Newspapers
Instructor:Stephen ScottMeeting Time and Place: Monday, Wednesday, Friday 10:30-11:20, Avery 112
Avery 364
sscott AT cse
http://www.cse.unl.edu/~sscott/
Biology easily has 500 years of exciting problems to work on.
- Donald E. Knuth, Professor Emeritus, Dept. of Comp. Sci, Stanford University
Bioinformatics is a discipline that employs computational sciences in molecular biological sciences. The need for advanced computational biology tools is fueled by an explosion in the rate of genomic data acquisition and the absence of robust computational methods for storing and analyzing the information. Even analysis of relatively small genomes, such as bacteria, encounters a significant bottleneck at the level of genome annotation (identifying genes and ascribing function) which is due in part to limitations in computational methods.
In this course you will learn several fundamentals and current trends in bioinformatics. As such, this course will not show you how to use existing computational biology tools, though you will probably learn some of that on your own as a side effect. Instead you will acquire a deep understanding of how they work, to the point where you can adapt existing tools to new problems and create new tools.
The biological problems we will study include sequence alignments, protein family modeling, and phylogeny. The approach we will focus on is hidden Markov models, though time permitting we will also discuss dynamic programming as well as machine learning models like decision trees and artificial neural networks.
Grades in this course will be based on homework exercises, writing exercises, and a project.
For more information, see the previous offering's web page: http://cse.unl.edu/~sscott/teach/Classes/cse496F02/