Manolis Kellis
@manoliskellis1·24.9K subscribers·255 videos
MIT CompBio Lecture 11 - Network inference and analysis (Fall '19)
Posted
October 10, 2019
Views
1,697
Likes
23
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1.36%
October 10, 2019
1,697
23
1.36%
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1:16:12Now PlayingMIT Computational Biology: Genomes, Networks, Evolution, Health
Prof. Manolis Kellis
Full playlist with all videos in order is here
All slides from Fall 2019 are here
Outline for this lecture:
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: eigenvalues, SVD, low-rank approximations
- 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
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