Carnegie Mellon University

Computational Genomics

Course Number: 02-510

Dramatic advances in experimental technology and computational analysis are fundamentally transforming the basic nature and goal of biological research. The emergence of new frontiers in biology, such as systems biology, is demanding new methodologies that can confront quantitative issues of substantial computational sophistication. In this course we will discuss classical approaches and latest methodological advances in the context of genomics and systems biology.
From the computational side this course focuses on modern machine learning methodologies for computational problems in molecular biology.

  • Sequence alignment
  • High-throughput sequencing data analysis
  • Analysis of gene expression data
  • Epigenetics and genome organization
  • Single cell data analysis
  • Complex biological networks
  • Application to specific biological processes and diseases

Semester(s): Spring
Units: 9/12
Prerequisite(s): 10-701 / 10-601 / 10-401 / 10-715 (Machine Learning), or an equivalent class.

Assessment Structure:
  • Homework assignments (40%)
  • Midterm exam (30%)
  • Project (25%)
  • Class participation (5%)