Download 0368-4137-01

Tel Aviv University Blavatnik School of Computer Science
Analysis of DNA Chips and Gene Networks
0368-4137-01
Ron Shamir
Course Description
DNA chips and micro-arrays have emerged over the last several years as powerful tools
to measure the expression levels of thousands of genes in a living cell or tissue. For the
first time, these techniques give a comprehensive picture of the levels of all genes
simultaneously. The challenge of understanding and using this data raises very exciting
and challenging mathematical problems. The course will deal with new and emerging
techniques for analyzing such data. Mathematical description of problems and
algorithms will be accompanied by examples of application to real problems in biology
and medicine.
As some of the cutting edge research in the field is carried out in Israel, the course will
include several guest lectures by leading experts.
Prerequisites
The course is open to all graduate students in computer science. No prerequisites are
needed beyond graduate standing in CS. Interested undergraduate students, as well as
non-CS students, should contact the instructor. The course requires no prior knowledge
in biology. All background will be provided in the lectures.
Course Outline
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Introduction: basic biological concepts, DNA chips technology
Clustering algorithms:
o Hierarchical clustering, k-means, self organizing maps, principal
components analysis; HCS, CLICK, BioClust;
o Applications: gene families, finding promotes, etc.
Classification
o Class prediction and class discovery; feature selection; Supervised
methods, SVM
o Cancer classification
Biclustering
o Cheng-Church's technique, CTWC, Signature method, SAMBA
Promoter Analysis
o Motif finding, PRIMA
o De-novo motif finding : WEEDER, MEME
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Genetic networks
o Kauffman's model, binary network models
o Network reconstruction; Experiment design
Bayesian networks
Protein interaction networks
Course requirements and grades:
The course has no class final exam - the grade is based on the following components:
1. Exercises
3-4 exercise sets will be given during the course. The exercieses will consists mostly of
theoretical questions and will also include a component of practical analysis of biological
data using methods studied in the course. Solutions should be done independently by
each student and without help from others. Use of books and articles for the solutions is
allowed and will not affect the grading, but the sources should be noted in the solutions.
Most assignments contain built-in bonus, so by completing some 90% worth of all
exercises you will be given full score.
2. Programming project
Students will be assigned a project which will require writing code (implementing
algorithms from the literature), applying it to public gene expression datasets and
reporting the results. The project can be done in pairs.
3. Scribe
While some lectures already have good scribes, other lectures will require revised or
new lecture notes. Pairs of students will scribe (i.e., to prepare lecture notes for) one
lecture. The notes should be prepared in LaTeX, and corrected according to guidelines
and marks given by the TA and/or the instructor. Precise instructions and schedule are
given here. Notes should contain all the material presented in class, written in clear and
accurate fashion, as well as the relevant references. Using figures and diagrams when
necessary in order to clarify things is recommended. In most lectures, the scribes of
lectures given in a previous year in the course can be used as a basis.
Breakdown of final grade:
With Scribe:
1. Exercises: 60%
2. Programming Project: 20%
3. Scribe: 25%
Without Scribe:
1. Exercises: 70%
2. Programming Project: 30%
Bibliography:
The course relies on recently published papers and there is no textbook. Scribes of the
lectures are available at http://www.cs.tau.ac.il/~rshamir/ge/09/ (~400 pages). See the
bibliography at the end of each scribed lecture.