  The identification of co-regulated genes and their transcription-factor
binding sites (TFBS) are the key steps toward understanding transcription
regulation. In addition to effective laboratory assays, various bi-clustering
algorithms for detection of the co-expressed genes have been developed.
Bi-clustering methods are used to discover subgroups of genes with similar
expression patterns under to-be-identified subsets of experimental conditions
when applied to gene expression data. By building two fuzzy partition matrices
of the gene expression data with the Axiomatic Fuzzy Set (AFS) theory, this
paper proposes a novel fuzzy bi-clustering algorithm for identification of
co-regulated genes. Specifically, the gene expression data is transformed into
two fuzzy partition matrices via sub-preference relations theory of AFS at
first. One of the matrices is considering the genes as the universe and the
conditions as the concept, the other one is considering the genes as the
concept and the conditions as the universe. The identification of the
co-regulated genes (bi-clusters) is carried out on the two partition matrices
at the same time. Then, a novel fuzzy-based similarity criterion is defined
based on the partition matrixes, and a cyclic optimization algorithm is
designed to discover the significant bi-clusters at expression level. The above
procedures guarantee that the generated bi-clusters have more significant
expression values than that of extracted by the traditional bi-clustering
methods. Finally, the performance of the proposed method is evaluated with the
performance of the three well-known bi-clustering algorithms on publicly
available real microarray datasets. The experimental results are in agreement
with the theoretical analysis and show that the proposed algorithm can
effectively detect the co-regulated genes without any prior knowledge of the
gene expression data.
