Evaluating the performance of partitioning techniques for gene network inference

Research in systems biology integrates experimental, theoretical, and modeling techniques to study and understand biological processes such as gene regulation. The genomic sequences for human and other model organisms such as yeast and bacteria are already established. The next major step is to discover functional roles of genes whose functions are not yet discovered and to investigate how genes interact with each other to perform different biological processes. DNA microarray technology provides access to large-scale gene expression data which are necessary for understanding functional role of genes and how genes interact on a global scale. Gene network reconstruction is one of the major research areas in Systems Biology. Modeling gene network systems will generate useful hypothesis about novel gene functions. Clustering gene expression data is used to analyze the result of microarray study. This method is often useful in understanding how a class of genes performs together during a biological process. Therefore, the purpose of this research is to investigate different clustering algorithms used in this paper including k-means clustering, fuzzy c-means and self-organizing maps (SOM). Clusters that are produced from these methods are then used to develop the graphical model using Bayesian Network (BN). Experiment results from the clustering methods are considered towards the statistical validation and then compared with each other. From out experiments, we found that SOM is better than k-means and fuzzy c-means since it produced the highest total number of clusters.