PMC:1687185 / 37411-38459 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":"Our results show that by branching out and discovering multiple local optimal solutions, higher m values are not needed. A higher m value corresponds to more computational time because projecting the l-mers into k-sized buckets is a time consuming task. Using our approach, we can replace the need for randomly projecting l-mers repeatedly in an effort to converge to a global optimum by deterministically and systematically searching the solution space modeled by our dynamical system and improving the quality of the existing solutions. We can see that for higher length motifs, the improvements are more significant. Fig. 4 shows the Tier-1 solutions obtained from a given consensus pattern. Since the exit points are being used instead of saddle points, our method might sometimes find the same local optimal solution obtained before. As seen from the figure, the Tier-1 solutions can differ from the original pattern by more than just one nucleotide position. Also, the function value at the exit points is much higher than the original value.","tracks":[]}