Towards gene network estimation with structure learning
Gene network is a representation of gene interactions. A gene usually collaborates with other genes in order to function. Understanding these interactions is a crucial step towards understanding how our body functions. Bayesian Network is a technique that was initially used in Expert System to rep...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2006
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| Subjects: | |
| Online Access: | http://eprints.utm.my/8019/1/SafaaiDeris2006_TowardsGeneNetworkEstimationWith.pdf |
| Summary: | Gene network is a representation of gene interactions. A gene usually collaborates with other genes in order
to function. Understanding these interactions is a crucial step towards understanding how our body
functions. Bayesian Network is a technique that was initially used in Expert System to represent expert
knowledge. Since the pioneer work of Friedman et al. that applied this technique to analyse gene
expression data, other researchers have enhanced the technique further. This research concentrates on
enhancing Bayesian Network technique fro learning gene network. In order to get better results, Bayesian
technique will be used with prior knowledge. The tool that is used to learn the gene network is
PNL(Probabilistic Network Library). Early results show that PNL can be used to recover gene network for
3 subnetworks for S.Cerevisiae. These 3 subnetworks has been learned using PNL with varying success.
The next step in this research is to learn the gene network from the dataset of 800 genes. The knowledge
that will be gained will be used to produce a better approach to learning gene network using Bayesian
network technique |
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