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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 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 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.