Improving Accuracy of Discovered Knowledge through Direct Interaction and Cohesion-based Framework: A Study in Cell Cycle Data of Yeast

Association mining tasks, when put to microarray data, normal trend is to highlight amount of discovered knowledge while quality analysis goes to backseat. Ideally, two more information is equally important: a) accuracy of knowledge extracted in a rule with respect to known biological functions, and b) predictability of biological interactions from discovered rules. Most of the support and/or confidence-based techniques address only predictability or neither of them. It requires tedious post-processing to unearth the actually interesting ones from the bulky output set. In the present work, we exploit the notion of direct interaction (DI) and cohesion to develop a sound methodology for binding genes under common affinity groups and mine intra-group associations. To evaluate soundness, we apply the method in cell cycle data of yeast and analyze result with the help of known biological interactions in BIND. We found impressive values for both accuracy and predictability.