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Knowledge Based Reconstruction of the Transcriptional Regulatory Network in Yeast

After genome sequence is revealed, it becomes the significant issue to discover the function of genes. The transcriptional regulatory network (TRN), which describes interactions between genes, is the key point to reveal the function of genes. The TRN can be constructed from the experimental data such as gene expression microarray data. Many computational and statistical methods using various resources have been studied to construct the TRN. But none of them is quite successful because biology system is very complex and its experimental results are affected largely by environment and noise. For example, supervised learning approaches using SVM by Qian et. al.[Qian2003] is robust, but it does not consider network features by multiple TFs. Bayesian network approach by Friedman et. al.[Friedman2000] finds network structure, but it is based on joint probabilities which are hard to be obtained from small number of training sets. In this project, we would like to provide network learning model using SVM and compare it with the previous SVM approaches.