Then it computes, for each extracted frequent motif, its support (π) and weighted support (πw) in the shuffled set. For a given frequent motif ℳ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBamrtHrhAL1wy0L2yHvtyaeHbnfgDOvwBHrxAJfwnaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaWaaeGaeaaakeaaimaacqWFZestaaa@3790@, let μ and σ be the mean and standard deviation of its support across different sets (about 30) of shuffled sequences. Then the Z-score for each motif is calculated as: Z=π(ℳ)−μσ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBamrtHrhAL1wy0L2yHvtyaeHbnfgDOvwBHrxAJfwnaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaWaaeGaeaaakeaatuuDJXwAK1uy0HwmaeXbfv3ySLgzG0uy0Hgip5wzaGqbaiab=Lr8Ajabg2da9maalaaabaacciGae4hWdaNaeiikaGccdaGae03mH0KaeiykaKIaeyOeI0Iae4hVd0gabaGae43Wdmhaaaaa@4BE7@. Likewise we can also calculate the Z-score for each frequent motif by using the weighted support (which is also applicable for the repeated structured motif identification problem). As shown in Table 5, we can successfully predict GAL4, GAL4 chips, LEU3, PPR1 and PUT3 with the highest rank. CAT8 and LYS also have high ranks. We were thus able to extract all eight transcription factors for the Zinc factors with high confidence. As a comparison, with the same dataset RISO can only predict GAL4, LEU3 and PPR1.