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The identification of co-regulated genes and their transcription-factorbinding sites (TFBS) are the key steps toward understanding transcriptionregulation. In addition to effective laboratory assays, various bi-clusteringalgorithms for detection of the co-expressed genes have been developed.Bi-clustering methods are used to discover subgroups of genes with similarexpression patterns under to-be-identified subsets of experimental conditionswhen applied to gene expression data. By building two fuzzy partition matricesof the gene expression data with the Axiomatic Fuzzy Set (AFS) theory, thispaper proposes a novel fuzzy bi-clustering algorithm for identification ofco-regulated genes. Specifically, the gene expression data is transformed intotwo fuzzy partition matrices via sub-preference relations theory of AFS atfirst. One of the matrices is considering the genes as the universe and theconditions as the concept, the other one is considering the genes as theconcept and the conditions as the universe. The identification of theco-regulated genes (bi-clusters) is carried out on the two partition matricesat the same time. Then, a novel fuzzy-based similarity criterion is definedbased on the partition matrixes, and a cyclic optimization algorithm isdesigned to discover the significant bi-clusters at expression level. The aboveprocedures guarantee that the generated bi-clusters have more significantexpression values than that of extracted by the traditional bi-clusteringmethods. Finally, the performance of the proposed method is evaluated with theperformance of the three well-known bi-clustering algorithms on publiclyavailable real microarray datasets. The experimental results are in agreementwith the theoretical analysis and show that the proposed algorithm caneffectively detect the co-regulated genes without any prior knowledge of thegene expression data.

A Novel Fuzzy Bi-Clustering Algorithm with AFS for Identification of Co-Regulated Genes