A Type-2 Fuzzy Method for Identification of Disease-related Genes on Microarrays

Abstract

Fuz zy set theory has been widely used in the analysis of gene microarray data. However, due to noise and uncertainty inherent in microarray data, traditional fuzzy methods sometimes do not perform well. In this paper, we propose a type-2 fuzzy membership test (Type-2 FM test) for disease-associated gene identification on microarrays to improve traditional fuzzy methods. We apply this method on diabetes and lung cancer microarrays and make a comparison with traditional fuzzy methods. For diabetes data, we can identify 7 genes which have been confirmed to be related to diabetes treatment in the published literature and one more gene can be identified than original approaches. For lung cancer data, we can also identify 7 genes which have been confirmed to be associated with lung cancer treatment in published literature and the type-2 d-values are significantly different. The results show that our type-2 FM test performs better than traditional fuzzy methods when analyzing microarray data with similar expression values and noise.