BGT 08 is the working number for a new course in the Graduate Certificate Program in Bioinformatics & Genome Technology. The program is jointly administered by the Center for Bioinformatics & Computational Biology and the Center for Genome Technology.

BGT 08 is being taught as STA 293B Special Topics in Statistics, in Spring semester 2001, the first time for this new course.

Register under course number:	STA 293B (Spring 2001 only)
Schedule:	12:40-1:55pm, Tues/Thur
Places:	025 Old Chemistry Building (primary classroom) & 208/389 CARL Building (labs and occasional class)
Instructors:	Joe Nevins, Department of Genetics Mike West, Duke Statistics

Prerequisites for this course include BGT.04 and BGT.06 or equivalent. Facility with statistical modelling at the level of STA 214, 244 or equivalent is desirable, and facility in applied computing (e.g., using Matlab or Splus) required. The course will require students to develop analyses of expression data using various techniques, and programming is necessary for this; we will be using Matlab this semester, and providing support for that, though other environments (such as Splus) could be used. This module covers topics spanning the technological and computational areas of modern gene expression analysis using DNA microarrays. Topics will include statistical methods in important and current problems of clinical and physiological phenotyping, and including custom computation and algorithmic development.

Course assessment will be based on a project presented as a term paper

Topics:

• Genetic expression: measurement, technology and applications
• DNA microarrays - technology, data and analytics
• Statistical and computations issues, models and methods for DNA microarray data mining:
  - graphical and numerical exploratory data analysis
  - classification and discrimination
  - binary prediction and regression analysis
  - statistical inference and computation for microarray data analysis

  Examples in ...

  - phenotyping: understanding genes implicated in clinical and physiological outcomes

  - cancer classification (breast cancer, leukemia, ..)

  - pattern analysis in cell cycle regulations, and other areas