Statistical Machine Learning Methods for Bioinformatics VII ...

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Challenges of Gene Bayesian Network • Massive number of variables (genes) • Small number of samples (dozens) • Sparse networks (only a small number of genes directly affect one another) • Two crucial aspects: computational complexity and statistical significance of relations in learned models N. Friedman, 2005
Solutions • Sparse candidate algorithm (by Nir Friedman): Choose a small candidate set <strong>for</strong> direct influence <strong>for</strong> each gene. Find optimal BN constrained on candidates. Iteratively improve candidate set. • Bootstrap confidence estimate: use re-sampling to generate perturbations of training data. Use the number of times a relation (or feature) is repeated among networks learned from these datasets to estimate confidence of Bayesian network features.
Page 1 and 2: Statistical Machine Learning Method
Page 3 and 4: Definition of Graphical Model • P
Page 5 and 6: A C B A ┴ B | C
Page 7 and 8: Comparison of Directed and Undirect
Page 9 and 10: Conditional Probability Distributio
Page 11 and 12: Compact Representation of Joint Pro
Page 13 and 14: P(W=1) is a normalizing constant, e
Page 15 and 16: Top-Down and Bottom-Up Reasoning
Page 17 and 18: In the first column, when we have t
Page 19 and 20: Example A B A B C C D Is A independ
Page 21 and 22: Dynamic Bayesian Networks • DBNs
Page 24 and 25: Linear Dynamic Systems (LDSs) and K
Page 27 and 28: Efficient Inference Algorithms •
Page 29 and 30: • The principle of distributing s
Page 31 and 32: Local Message Passing • If the BN
Page 33 and 34: Learning of BN • The graph topolo
Page 35 and 36: Known Structure, Partial Observabil
Page 37 and 38: General Application Examples • Mi
Page 39 and 40: Discovery of Regulatory Mechanism /
Page 41: Gene Bayesian Network Gene E Qualit
Page 45: Important References: BN in Bioinfo

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