Microarrays have allowed biologists to better understand gene regulatory mechanisms. Wheat microarray data analysis is a complex and challenging topic and knowledge of gene regulation in wheat is still very superficial. However, understanding key mechanisms in this plant holds much potential for food security, especially with a changing climate. The purpose of this paper is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions. For example, identifying a regulatory network of genes that only exists under certain types of experimental conditions will assist in understanding the nature of the mechanisms. We derive unique networks from multiple independent networks to better understand key mechanisms and how they change under different conditions. We compare the results with biclustering, detect the most predictive genes and validate the results based upon known biological mechanisms. We also explore how this pipeline performs on yeast microarray data.
['Valeria Bo', 'Artem Lysenko', 'Mansoor Saqi', 'Dimah Z. Habash', 'Allan Tucker']
Integrating Multiple Studies of Wheat Microarray Data to Identify Treatment-Specific Regulatory Networks