Combining Clustering and Bayesian Network for Gene Network Inference

Gene network reconstruction is a multidisciplinary research area involving data mining, machine learning, statistics, ontologies and others. Reconstructed gene network allows us to understand how genes interact with each other. However, network construction is very complex due to highly interactive nature of genes. A proposed approach to solve this complex problem is to cluster the genes according to similarity in their gene expression values. We applied k means clustering with k = 10 to come up with ten clusters of genes. Then, we applied Bayesian Network structure learning with Hillclimbing search strategy and Akaike Information Criterion score to search for the best network. We compared inferred interactions to a reference positive interactions dataset and found similarity between our inferred interactions and the reference. We further study the gene interactions using Gene Ontology. From our findings, we conclude that the clustering step is essential in gene network reconstructio...

• Type: Conference
• Year: 2008
• Where: ISDA
• Authors: Suhaila Zainudin, Safaai Deris
• Keywords: Gene, Gene Network, Gene Network Reconstruction, ISDA 2008, Operating Systems