Special Topics in Computer Science:
Bioinformatics
CIS 595 – Spring 2005
Meeting days:
Monday, 7:25P - 9:55P, TL302 Instructor: Slobodan
Vucetic, 304 Wachman Hall, phone: 204-5535,
www.ist.temple.edu/~vucetic Office Hours:
Wednesday 3:00 pm - 4:00 pm, Friday 3:00pm - 4:00pm, or by appointment Objective:
Bioinformatics is an interdisciplinary field that involves developing and
applying computational methods for managing and analyzing information about the
sequence, structure and function of biological molecules and systems. Main goals
of this course are to provide an understanding of the types and sources of
biological data, the fundamental computational problems in molecular biology and
genomics, and a core set of widely used algorithms in computational biology.
Existing methods will be critically described and the strengths and limitations
of each will be discussed. Future directions for development of new methods will
also be discussed. Students will get hands-on experience through using various
bioinformatics software. audience: The course is designed to introduce bioinformatics at a level
appropriate for computer science students, although it could be useful to
biology, chemistry, and statistics students.
prerequisites:
CIS511 or permission from the instructor. Knowledge of data mining/machine learning/statistics and algorithms is highly desirable. Knowledge of biology/molecular biology/biochemistry is a plus.
textbooks:
No required textbook
Papers and handouts relevant to presented topics will be distributed as needed.
Recommended textbooks:
1. Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison, Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Cambridge University Press, 1999.
2. David W. Mount, Bioinformatics: Sequence and Genome Analysis, Cold Spring Harbor Laboratory Press, 2001.
3. Greg Gibson, Spencer V. Muse, A Primer of Genome Science, 2nd edition, Sinauer Associates, 2004.
Topics:
Pairwise Sequence Alignment.
Multiple Sequence Alignment.
Database Searching for Similar Sequences.
Phylogenetic Analysis.
Machine Learning Algorithms for Bioinformatics.
Protein Classification, Structure, and Function Prediction.
Microarray Data Analysis.
Gene Prediction.
Genome Analysis.
Modeling Cellular Systems.
Biomedical Text Analysis.
Emerging
Directions in Bioinformatics Research.
Grading:
Class Participation 10%, Homework (exercises, reading assignments) 25%, Class Presentation 15%, Midterm 20%, Individual Project 30%.
LATE POLICY AND ACADEMIC HONESTY:
The projects and homework assignments are due in class, on the specified due date. NO LATE SUBMISSIONS will be accepted. For fairness, this policy will be strictly enforced. Academic honesty is taken seriously. You must write up your own solutions and code. For homework problems or projects you are allowed to discuss the problems or assignments verbally with other class members or instructor. You MUST acknowledge the people with whom you discussed your work. Any other sources (e.g. Web, research papers, books) used for solutions and code MUST also be acknowledged.
DISABILITY DISCLOSURE STATEMENT:
Any student who has a need for accommodation based on the impact of a disability should contact me privately to discuss the specific situation as soon as possible. Contact Disability Resources and Services at 215-204-1280 in 100 Ritter Annex to coordinate reasonable accommodations for students with documented disabilities.