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Advanced Algorithms and Models for
Computational Biology
02-710/MSCBIO2070, Spring 2007
School of Computer
Science, Carnegie-Mellon
University
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Grading
- The requirements of
this course will consist of participating in lectures, four problem
sets, a midterm, and a project. This is a graduate class, and the most
important
thing for us is that by the end of this class students will be aware of
the recent advance in computational biology, and of the challenges
ahead. Still, as any class this course will be graded. The grading
breakdown is the following:
- Class participation and reading (10%)
- Homework (4 assignments, 36% in
total and 9% each)
- Midterm (24%)
- Final project (30%).
Class participation and reading
For the first part of the course we will be using: Biological Sequence
Analysis by Durbin, Eddy, Krogh and Mitchison (Cambridge press).
However, since computational genomics is a rapidly evolving field,
there is currently no text book that covers all the material in this
course. Most lectures will have assigned reading, and it is expected
(and required) that students read the assigned papers before class.
Most reading assignments will be recent papers in scientific journals
or conferences. Initially, you may find some of these papers hard to
read (either because of the biological terms or because of the
computational methods, depending on your background). However, as the
course progresses our hope is that these reading assignments will
become easier, so that by the end of the term you be able to read (and
understand) papers from Science, Nature and PNAS (or RECOMB, depending
on your background).
Homework resources and collaboration
policy
Homeworks and the exam may contain material that has been covered
by papers and webpages. Since this is a graduate class, we expect
students
to want to learn and not google for answers.
Homeworks will be done individually:
each student
must hand in their own answers. It is acceptable, however, for
students
to collaborate in figuring out answers and helping each other solve the
problems. We will be assuming that, as participants in a graduate
course, you
will be taking the responsibility to make sure you personally
understand the
solution to any work arising from such collaboration. You also must
indicate on each homework with whom you
collaborated.
The final
project may be completed by small teams.
Late
homework policy
- You will be allowed 3 total late days
without penalty for the entire semester. For instance, you may be late
by 1 day on three different homeworks or
late by 3 days on one homework. Once those
days are used, you will be penalized according to the policy below:
- Homework is worth full credit at the
beginning of class on the due date.
- It is worth half credit for the next
48 hours.
- It is worth zero credit after that.
- You must turn in all of the 5 homeworks, even if for zero credit, in order to
pass the course.
- Turn in all late homework assignments
to Monica Hopes.
Homework regrades policy
If you feel that we have made an error in grading your homework,
please turn
in your homework with a written explanation to Monica
Hopes,
and we will consider your request. Please
note that regrading of a
homework
may cause your grade to go up or down.
Homework
assignments
We will have four problem sets. Problem sets will consist of both,
theoretical and programming problems. This is not a computer systems
class, and so the programming load will be small. Still, we think that
it is essential to work with real data since computational biology is
an applied field. We will use matlab for the programming part, and we
will have an ‘intro to matlab’ class for those who did not work with
matlab in the past.
Final project
Computational biology faces many challenges, and it is possible to
arrive at very interesting results in a (relatively) short time.
Projects will either try to extend one the methods discussed in class,
apply a method to a new dataset or apply a new / revised algorithm to
one of the problems discussed in class (for example, a new clustering
or classification algorithm). If you are working on a new machine
learning / graph theoretical or biological problem, and you think it
can be applied to one of the problems we discussed, you can base your
project on this problem. Projects will be done in groups of up to three
people. I am hoping to have a good mix of students, so that
heterogeneous teams of students from different departments can form.
The projects will include a writeup (of 6-8 pages) and a class
presentation. We will hold preliminary class presentations towards the
last third of the class. I encourage you to form teams as early as
possible, and start working on a problem as soon as you can. Once you
have formed a team, you are welcomed to schedule a meeting with me to
discuss possible projects.
For project milestone, roughly half of the project
work
should be completed. A short, graded write-up will be required, and we
will
provide feedback.
Note to people outside CMU
Feel free to use the slides and materials
available online
here. Please email the instructors with any corrections or
improvements.