Description
Material
External Info
Description

This course surveys some microarray data analysis methods, pattern discovery, clustering and classification methods, applications to prediction of clinical outcome and treatment response, coding region detection and protein family prediction. Included are some statistical methods for evaluating performance, Fisher's exact test, Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, odds ratio, risk ratio, Matthews' correlation coefficient, 
Mann-Whitney test, Wilcoxon test, and receiver operating characteristic curves. This course uses linear algebra from APSC-174, and probability theory from ELEC-326.

Course Learning Outcomes (CLOs)
  1. You will understand some approaches related to individualizing medical treatment
  2. You will be able to apply some of the class prediction methods to real microarray data
  3. You will learn some practical, frequently-used techniques, such as Principal Component Analysis (PCA)
  4. You will learn an alternative to PCA, and see how it has been applied to more traditional engineering problems
  5. You will learn some methods for reducing stochastic errors in commonly used devices, such as MEMS inertial sensors in smart phones
Credit Breakdown

Lecture: 3
Lab: 0
Tutorial: 0

Academic Unit Breakdown

Mathematics 9
Natural Sciences 0
Complementary Studies 0
Engineering Science 18
Engineering Design 9

PREREQUISITE(S): APSC 174ELEC 323 and ELEC 326 or ENPH 252