MIT CompBio Lecture 11 - Network Analysis

MIT Computational Biology: Genomes, Networks, Evolution, Health
Prof. Manolis Kellis
Fall 2018

Lecture 11 - Networks I and Inference

1. Introduction to networks
- Network types: regulatory, metab., signal., interact., func.
- Bayesian (probabilistic) and Algebraic views
2. Network Centrality Measures
- Local centrality metrics (degree, betweenness, closeness, etc)
- Global centrality metrics (eigenvector centrality, page-rank)
3. Linear Algebra Review
- Eigenvector and singular vector decomposition
Low rank approximations, Wigner semicircle law
4. Sparse Principal Component Analysis
- Lasso and Elastic lasso
- PCA and Sparse PCA
5. Network Communities and Modules
- Guilt by association
- Maximum cliques, density-based modules and spectral clustering
6. Network Diffusion Kernels and Deconvolution
- Network diffusion kernels
- Network deconvolution

Slides: