PMC:1687185 / 15064-15883 JSONTXT

{"project":"2_test","denotations":[{"id":"17129371-12651719-1691525","span":{"begin":630,"end":632},"obj":"12651719"}],"text":"where Qk, j/Qb represents the ratio of the nucleotide probability to the corresponding background probability. Log(A)i is the score at each individual ith sequence. In equation (4), we see that A is composed of the product of the weights for each individual position k. We consider this to be the Information Content (IC) score which we would like to maximize. A(Q) is the non-convex 3l dimensional continuous function for which the global maximum corresponds to the best possible motif in the dataset. EM refinement performed at the end of a combinatorial approach has the disadvantage of converging to a local optimal solution [22]. Our method improves the procedure for refining motif by understanding the details of the stability boundaries and by trying to escape out of the convergence region of the EM algorithm."}