We also considered the differential expression of all
We also considered the differential Epirubicin HCl of all genes in a bicluster. For each coding gene or miRNA in a cancer-specific bicluster, if it is differentially expressed, we set dei = 1; otherwise, we set dei = 0. To identify differential expression genes that may contribute to the malignant phenotype of breast cancer, the DESeq2 was used  under the default parameter setting, and all coding genes and miRNAs with adjust p-value < 0.001 are deemed as differentially expressed.
Many known cancer-related coding genes and miRNAs provide a valuable source to predict other potential cancer biomarkers or bio-markers of a specific cancer type. It was found that genes interacting with known cancer-related genes were shown to be ten-fold enriched in true cancer-causing modules . Therefore, we used known cancer-related coding genes and miRNAs as heuristics as many others did [10,14,44]. If a coding gene or miRNA in a cancer-specific bicluster is a known cancer-related gene or miRNA, we set kci = 1; otherwise, we set kci = 0. Furthermore, we consider whether a coding gene has direct interactions with known cancer-related genes in the PPI network. Since genes with direct interactions in the PPI network are likely functional related, genes having direct interactions with known cancer-related coding genes in the HPRD  network are more likely cancer-related. We define the average direct interaction number DIi of a coding gene in a cancer-specific bicluster in Eq. (8).
where NK is the total number of known cancer-related coding genes in the HPRD network, and diij = 1 if coding gene i has a direct interaction with coding gene; otherwise, diij = 0.
For any coding gene i (or miRNA) in a cancer-specific bicluster, we define bicluster coding gene or miRNA importance value IMi in Eq. (9). IMi = dc i + dei + DIi + kci (9) In this way, we integrate diverse information together and get a more robust measure of the importance value for a coding gene or miRNA. If a gene or miRNA appears in more than one biclusters, we use the biggest value as its importance value.
2.4. Rank fusion process
Rank fusion creates a comprehensive rank by combining multiple rank results together. To do this, firstly, all cancer-specific biclusters are ranked according to their absolute mean Z-score value, and then all coding genes and miRNAs in each breast cancer-specific bicluster are ranked according to their importance values. To get the final global rank, we adapted the rank fusion process used in [18,46]. The rank fusion method is a recursive process, which decides the rank of the n th gene based on the pre-ranked n 1 ones.
We use n to represent the number of genes to be ranked. b (n , i) represents the number of top n genes located in the bicluster i. t(n,i) is the estimated number of top n genes in the bicluster i. e(n,i) is the ex-pected value that the n + 1 th ranked gene comes from the bicluster i. The absolute mean Z-score of a bicluster z(mi) is used to indicate the probability of a coding gene or miRNA to be breast cancer-related. The relationship among n, b(n,i), e(n,i) and z(mi) is shown in Eq. (10).
2.5. Cancer-related miRNA-gene interaction detection
To detect breast cancer-related miRNA-gene interactions, only breast cancer-specific biclusters including both coding genes and miRNAs are used. For coding genes and miRNAs in each bicluster, we represent the number of miRNA-gene interactions recorded in the miRTarBase database  as KI. The miRTarBase database contains only experimentally validated miRNA-gene interactions. The miRNA-gene interaction significance value p of each bicluster is defined in Eq. (11). p = nC (11)
where T represents number of random samplings. In each sampling we randomly select the same number of coding genes and miRNAs (non-repetitive sampling) in the bicluster considered, and represent the number of miRNA-gene interactions recorded in the miRTarBase data-base as ki . nC represents the number of times ki KI in T times of sampling. We use a p threshold 0.05 to select significant biclusters, and count the occurrence of all the miRNAs in all the significant biclusters. The miRNAs with highest occurrence and their corresponding gene interactions present in miRTarBase are selected as breast cancer-related miRNA-gene interactions.