We then eliminate all the predictions that are similar to this prediction, based on some similarity measure, using a specified cutoff. Of the remaining set, the prediction with the highest score becomes the center of the second cluster, and these steps are repeated until no predictions remain in the list. The resulting set of cluster centers represents a pruned set of predictions, which are spaced by at least the threshold. The clustering process is finalized by determining how many predictions of the original set are within the threshold distance of each cluster center. For the pruning using angular distance we also explored a ‘translation-restricted’ variant of the algorithm. Predictions that have a translational difference of more than half the receptor size are not allowed to be in the same cluster, as they are highly unlikely to belong to the same funnel. The translational difference is obtained from the three translational AbMole Cyclosporine coordinates in the rigidbody docking, and the receptor size is defined as the average of the lengths of the protein in the directions of the three Cartesian axes. Because the translational difference is needed only for pairs of predictions that have angular distances under the angular threshold, this extension to the algorithm only increases the computational time moderately. An alternative approach to score-based pruning is to rank and prune based on the density of predictions. We explored two versions of density-based pruning. First we followed the ClusPro algorithm, which determines for each prediction the number of neighbors within a threshold distance, ranks accordingly, and uses this rank for a pruning step. Second, we used R to hierarchically cluster the predictions, and varied the height at which the branches are cut to find the best performance. For both density-based algorithms we used the top scoring 2000 predictions as starting point, and tested both RMSD and angular distance. The ZDOCK score was used to rank predictions that have identical densities. For the hierarchical clustering we used the complete linkage method, and the defined the medoid as the prediction that represents a cluster. miRNAs are 18- to 25-nucleotide non-coding RNA molecules that regulate mRNA translation. They exert the effects by targeting the RNA-induced silencing complex to complementary sites in the 39 untranslated region of their target genes. Binding of a miRNA-loaded RISC to a complementary sequence will lead to either translational repression or decay of the targeted mRNA. Through this, miRNAs regulate a variety of cellular processes including apoptosis, differentiation and cell proliferation. Altered miRNA expression profiles were found in most tumor types including colorectal cancer. Manipulation of specific miRNAs was found to be able to modulate tumor development in animal model. Previously, through profiling the expression of 667 miRNAs in human colorectal cancer tissues, we identified AbMole Lomitapide Mesylate miR-18a as one of the most up-regulated miRNAs in human CRC. A high level of miR-18a can be detected in stool of CRC patients compared to individuals with normal colonoscopy. Upon removal of the tumor, stool level of miR-18a dropped significantly. miR-18a belongs to the miR-17-92 cluster, which is located at chromosome 13q31.1 region. The oncogenic role of the miR-1792 cluster is well documented. Over-expression of the cluster is associated with accelerated tumor growth and cell proliferation.