CS446/546, Networks in Computational Biology, Fall 2020

Instructor: Stephen Ramsey (email: ramseyst)

Date/Time: Tuesday/Thursday 4:00–5:20 PM (location TBD)

Course flyer: csx46.pdf

Number of credits: 3

CRN (CS446): CRN 38058

CRN (CS546): CRN 38059

Tentative syllabus:

  # pages plan for notebooks
  class01 Part 1: network fundamentals Course organization and policies; using JupyterHub syllabus notebook 2
  class02 Graph fundamentals and data structures Newman 6.intro, 6.1, 6.2, 6.3, 6.4, 6.7, 9.intro, 9.1, 9.2, 9.3, 9.4, 9.5, 9.6 39 notebooks 3-5
  class03 Degree & degree distribution; scale­free networks; attack tolerance Newman 6.9, 7.intro, 7.1, 8.3, 8.4, 10.intro, 10.1, 5.1.2, Jeong et al. Nature v411 pp41-42 (2001). 20 notebook 6
  class04 Clustering coefficients, density Newman 7.9, 8.6, 10.2, 5.1.3, Potapov et al. Genome Inform. v16(2) pp 270-274 (2005). 20 notebook 7
  class05 Paths, geodesic paths, diameter, components, depth­first Newman 6.10, 6.11, 8.intro, 8.1, 5.intro, 5.1.1 20 notebook 8
  class06 Single­vertex shortest­paths (Breadth­First); Dijkstras algorithm Newman 8.2, 10.3, Jeong et al., Nature v407 pp651-654 (2000). 17 notebook 9
  class07 Centralities Newman 7.2, 7.3, 7.4, 7.6, 8.5,  17 notebooks 10, 12
  class08 Betweenness centrality Newman 7.7, 10.3.6, Poulo Joy et al., J Biomed Biotechnol v2 pp96-103 (2005). 19 notebook 11  
  class09 similarity, topological overlap, assortative mixing, degree correlations (without GO) Newman 7.12, 7.13, 8.7 21 notebook 13
  class10 graph partitioning for community detection; date hubs, party hubs Newman 11.2, 11.11, Raval 8.2.2, Han et al. Nature v430 pp88-93 (2004) 19 notebook 17
  class11 Part 2: network inference Predicting protein-protein interactions Raval 3.5, 3.6 17
  class12 Inferring PPI, naïve bayes Raval 5.6; Rhodes et al. PLOS Comp Biol v23, n8 2005. 14
  class13 correlation networks Junker & Schreiber Chapter 13 (2008). 17 notebook 19
  class14 partial correlation de la Fuente et al., Bioinformatics v20, n18, pp3565-3574 (2004) 10 notebook 20
  class15 mutual information  Raval 3.1, 3.2, 3.3, 3.4; Basso et al. Nat Genet v37 n4 pp382-390 (2005) 24 notebook 21
  class16 bayesian network, MCMC Sachs et al., Science v308 pp523-529 plus supplement (2005). 14
  class17 Dynamic Bayesian network Raval 3.4; Rangel et al., Bioinformatics v20, n9, pp1361-1372 (2004). 22 notebook 26
  class18 Part 3: network analysis markov clustering Raval 5.4; Krogan et al. Nature v440 pp637-643 (2006). 24 notebook 14
  class19 cliques/cores, MCODE Newman 7.8, Bader & Hogue BMC Bioinform. v4 pp1-27 (2003). 15 notebook 15
  class20 boolean network, attractors, dynamical simulation Raval 6.4; Li et al. PNAS v101 n14 pp4781-4786 (2004) 17 notebook 27