Improving robustness of fuzzy gene modeling

This paper proposes modifications to current fuzzy models of gene interaction. Current algorithms apply all combinations of genes to a fuzzy model (i.e. activator/repressor/target), evaluating how well each combination fits the model. The models are susceptible to noisy signals in the gene expression data. Since the margin of error in current microarray technology can be high, the results generated may not properly reflect valid relationships. This paper investigates different methods of creating fuzzy models. We explore methods of conjunction and rule aggregation that produce valid results while being resilient to minor changes to model input.