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Gene regulatory network reconstruction by Bayesian integration of prior knowledge and/or different experimental conditions
Published on Nov 06, 20079331 Views
There have been various attempts to improve the reconstruction of gene regulatory networks from microarray data by the systematic integration of biological prior knowledge. Our approach follows the
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Chapter list
Which Sources of Prior Knowledge Are Reliable?01:47
Overview of the Talk01:55
Learning Bayesian Networks - 106:55
Learning Bayesian Networks - 207:42
Learning Bayesian Networks - 307:59
Overview of the Talk08:16
Approximation of the Partition Function - 113:47
Energy of a Network - 416:10
Approximation of the Partition Function - 216:26
MCMC Sampling Scheme16:42
Sample Networks and Hyperparameters from the Posterior Distribution17:03
Overview of the Talk20:18
Evaluation - 120:21
Application to the Raf Regulatory Network20:47
The Data and the Priors23:58
Bayesian Networks with Two Sources of Prior - 424:18
Bayesian Networks with Two Sources of Prior - 524:25
Sampled Values of the Hyperparameters24:28
Alternative Performance Evaluation: True Positive (TP) Scores - 126:34
Directed Graph Evaluation - DGE26:56
Undirected Graph Evaluation - UGE27:08
Reconstructing Regulatory Networks under Different Experimental Conditions32:33
The Original Problem of Reconstructing - 132:38
The Original Problem of Reconstructing - 232:45
Multiple Data Sets Obtained under Different Experimental Conditions - 132:46
Multiple Data Sets Obtained under Different Experimental Conditions - 232:47
Combining the Data33:42
Combining the Data - 133:43
Combining the Data - 234:14
Compromise - 134:15
Compromise - 235:28
Compromise - 335:34
Simulated Data - 436:28
Can We Detect the Corrupt (Noisy) Data Set? - 136:45
Can We Detect the Corrupt (Noisy) Data Set? - 236:51
Acceptance Ratios38:59