Motif discovery by monotone scores Original Research Article

The detection of frequent patterns such as motifs and higher aggregates is of paramount interest in biology and invests many other applications of automated discovery. The problem with its variants is usually plagued with computational burden. A related difficulty is posed by the fact, that due to the sheer mole of candidates, the tables and indices at the outset tend to be bulky, un-manageable, and ultimately uninformative. For solid patterns, it is possible to compact the size of statistical indices by resort to certain monotonicities exhibited by popular scores. The savings come from the fact that these monotonicities enable one to partition the candidate over-represented words into families in such a way that it suffices to consider and weigh only one candidate per family.