PMC:1687185 / 41517-42791 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/1687185","sourcedb":"PMC","sourceid":"1687185","source_url":"https://www.ncbi.nlm.nih.gov/pmc/1687185","text":"Table 5 shows the results of the Exit-point methodology on real biological sequences. We have chosen l = 20 and d = 2. 't' indicates the number of sequences in the real data. For the biological samples taken from [1,12], the value m once again is the average number of random projection + EM cycles required to discover the motif. All other parameter values (like projection size k = 7 and threshold s = 4) are chosen to be the same as those used in the Random projection paper [1]. All of the motifs were recovered with m = 1 using the Exit-point strategy. The Random Projection algorithm alone needed multiple cycles (m = 8 in some cases and m = 15 in others) in order to retrieve the correct motif. This elucidates the fact that global methods can only be used to a certain extent and should be combined with refined local heuristics in order to obtain better efficiency. Since the random projection algorithm has outperformed other prominent motif finding algorithms like SP-STAR, WINNOWER, Gibbs sampling etc., we did not repeat the same experiments that were conducted in [1]. Running one cycle of random projection + EM is much more expensive computationally. The main advantage of our strategy comes from the deterministic nature of our algorithm in refining motifs.","tracks":[]}