Tel Aviv University School of Computer Science


Analysis of DNA Chips and Gene Networks

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.

The course requires no prior knowledge in biology. All background will be provided in the lectures.

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.

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.

Course Outline

  • Introduction: basic biological concepts, DNA chips technology
  • Clustering algorithms:
    • Hierarchical clustering, k-means, self organizing maps, principal components analysis; HCS, CLICK, BioClust;
    • Applications: gene families, finding promotes, etc.
  • Classification
    • Class prediction and class discovery; feature selection; Supervised methods, SVM
    • Cancer classification
  • Biclustering
    • Cheng-Church's technique, CTWC,  Signature method, SAMBA
  • Promoter Analysis
    • Motif finding, PRIMA
    • De-novo motif finding : WEEDER, MEME
  • Genetic networks
    • Kauffman's model, binary network models
    • Network reconstruction; Experiment design
  • Bayesian networks
  • Protein interaction networks

 

rshamir AT tau.ac.il