Learning Bayesian Networks with microarray data. Goal: use well known Bayesian network learning algorithms to analyze microarray data. Challenge in microarray data analysis techniques. Prior techniques (clustering, PCA, SVM): Group together genes with similar expression patterns
Learning Bayesian Networks with microarray data
Goal: use well known Bayesian network learning algorithms to analyze microarray data
Gene-gene relation analysis
Activation or inhibition
Gene Regulatory network analysis
Constructed bayesian network
Global view on the relations among genes
Evidence: my car does not start.
Reasoning: now fuel and dirty spark plugs become more certain, therefore the certainty of the fuel meter standing for empty also increases.
The bayesian directed acyclic graph actually describes the joint probability of P(X1,X2,…,Xn):
P(X) = П P(Xi|Pa(Xi))
Where Pa(Xi) are the parents of node Xi
Friedman used a specialized learning method (SCA), permuted the dataset to learn 200 networks and selected some special features from these networks to create a final network.
Problem is that we compare ill VS healthy: big difference
Experiment for a few more months!