Comparison of Patser and Consite.

<p>Number of TFBSs from the dataset by Harbison et al. against the total number of TFBSs detected by Patser and Consite.</p

Top-20 TF combinations.

<p>First dataset. The twenty TF combinations with the lowest p-value and highest support obtained when using the dataset by Harbison et al. Evidence column shows whether results were yielded when PubMed was queried for evidence in the literature (P), STRING <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108065#pone.0108065-Franceschini1" target="_blank">[48]</a> yielded a connected graph for the given TFs (S), both conditions (SP) or none (-) were met.</p><p>Top-20 TF combinations.</p

Fuzzy-crisp comparison.

<p>The four first rows show the mean values of fuzzy/crisp support and <i>p</i>-value of the combinations respectively. The last two rows show the statistical significance returned by the ANOVA procedure.</p><p>Fuzzy-crisp comparison.</p

Post-processing of the results.

<p>The value indicates the membership degree of each binding site to its corresponding transaction. (a) Pairs of overlapping binding sites are directly removed. (b) The optimum way of fitting itemset {A, B, C} is found.</p

Parameter values.

<p>Second dataset. Summary of the input parameters used.</p><p>Parameter values.</p

TF combinations.

<p>Second dataset. Some of the TF combinations obtained when using the TFBSs detected by Patser (yeast genome).</p><p>TF combinations.</p

Outline of the CisMiner procedure.

<p>Diagram of the main steps of the CisMiner procedure. Given a set of TFBSs, the process starts by performing a fuzzy hierarchical clustering to obtain a set of closely located TFBSs. The result of this step is a fuzzy transactional database, which will then be mined by a Fuzzy Frequent Itemset Mining algorithm (Fuzzy Frequent-Pattern Tree) to obtain a set of frequent fuzzy itemsets. Finally, a postprocessing takes place in order to handle overlapping TFBSs that appear in each frequent itemset. As a result, a set of putative CRMs, along with their estimated p-value and their fuzzy support, is given.</p

Procedure for generating the fuzzy transactional database.

<p>(1) Each circle represents a binding site. Each binding site is labeled with the name of the TF which binds that BS. (2) Three clusters are obtained. Centroids are calculated for each cluster. (3) Fuzzy sets are defined for each cluster. (4) Fuzzy transactions are generated from the fuzzy sets. The value after the colon indicates the membership degree of the corresponding TF to the transaction.</p

Box plots representing expression values of FALZ, CAPG, CIR, NUPL2, PRDM2, and ZFP36 genes by quantitative real-time RT-PCR in both groups of rectal cancer patients defined by their response to treatment: responder (R), and non-responder (NR).

<p>FALZ, CAPG, CIR, NUPL2, PRDM2, and ZFP36 expression levels were successfully obtained from 30, 13, 29, 27, 30, and 23 LARC patients. Boxes represent the quartiles, median is represented by a black line within the box, and circles (0) show atypical values (1.5–3 times the length of the box). Asterisk (*) shows extreme values (more than three times the box). FALZ gene expression showed statistically significant differences between responder and non-responder patients.</p

Patients and tumour characteristics.

<p>CRT: Chemoradiation; Cap: Capecitabine; Capox: Capecitabine and Oxaliplatine; cTN: clinical stage, Surg: surgical technique, LAR: Low anterior resection, APR: Abdmino-perineal resection; HART: Hartmann, TRG: Tumor Regression Grade; Downst: Downstaging; Resp: response, Leuc: leucocytes (×10³×ml), Lymp: lymphocytes (%).</p