CSE 527 Computational Biology     [ Home Schedule Handouts Project ]



Lecture Notes

  • Syllabus [PDF]
  • Introduction
    • Course logistics, short intro to molecular biology (lecture 1) [PDF]
  • Introduction to probabilistic models for computational biology
    • Introduction to Bayesian networks (lecture 2) [PDF]
    • Parameter estimation, Maximum likelihood estimation (lecture 3) [PDF]
    • Expectation maximization algorithm (lecture 4) [PDF]
  • Systems biology
    • Microarray data analysis I - clustering (lecture 5) [PDF]
    • Microarray data analysis II - principal component analysis (lecture 6) [PDF]
    • Inferring gene regulatory network I - undirected graphical models (lecture 7) [PDF]
    • Inferring gene regulatory network II - directed graphical models (lecture 8) [PDF]
    • Regulatory motif finding I (lecture 9) [PDF]
    • Regulatory motif finding II (lecture 10) [PDF]
  • Genetics
    • Genetics basic, QTL mapping (lecture 11) [PDF]
    • Disease association studies (lecture 12) [PDF]
    • Genetics basics and haplotypes (lecture 13) [PDF]
    • Haplotype reconstruction I (lecture 14) [PDF]
    • Haplotype reconstruction II (lecture 15) [PDF]
  • Sequence analysis
    • Sequence alignment I (lecture 16) [PDF]
    • Sequence alignment II (lecture 17) [PDF]
  • Computational cancer biology
    • Basics on cancer biology (lecture 19) [PDF]
Problem Sets
  • Problem set #1  Out 10/3, due 10/17. [PDF]
  • Problem set #2  Out 10/17, due 10/31. [PDF]
  • Problem set #3  Out 10/31. due 11/14[PDF
  • Problem set #4  Out 11/14. due 11/28[PDF
Reading Materials