▁which ▁is ▁particularly ▁relevant ▁for ▁newly ▁discovered ▁ep igen etic ▁mechan isms ▁and ▁for ▁species ▁other ▁than ▁human ▁and ▁mouse . ▁Second , ▁prediction ▁algorithms ▁build ▁statistical ▁models ▁of ▁ep igen etic ▁information ▁from ▁training ▁data ▁and ▁can ▁therefore ▁act ▁as ▁a ▁first ▁step ▁toward ▁quant itative ▁model ing ▁of ▁an ▁ep igen etic ▁mechanism . ▁Success ful ▁computational ▁prediction ▁of ▁DNA ▁and ▁l ys ine ▁m eth yl ation ▁and ▁ac et yl ation ▁has ▁been ▁achieved ▁by ▁combinations ▁of ▁various ▁features . ▁ ▁App lications ▁in ▁cancer ▁ep igen et ics ▁The ▁important ▁role ▁of ▁ep igen etic ▁defect s ▁for ▁cancer ▁opens ▁up ▁new ▁opportun ities ▁for ▁improved ▁diagn osis ▁and ▁ther apy . ▁These ▁active ▁areas ▁of ▁research ▁give ▁rise ▁to ▁two ▁questions ▁that ▁are ▁particularly ▁am enable ▁to ▁bio in form atic ▁analysis . ▁First , ▁given ▁a ▁list ▁of ▁genom ic ▁regions ▁exhib iting ▁ep igen etic ▁differences ▁between ▁tum or ▁cells ▁and ▁controls ▁( or ▁between ▁different ▁disease ▁sub types ), ▁can ▁we ▁detect ▁common ▁patterns ▁or ▁find ▁evidence ▁of ▁a ▁functional ▁relationship ▁of ▁these ▁regions ▁to ▁cancer ? ▁Second , ▁can ▁we ▁use ▁bio in form atic ▁methods ▁in ▁order ▁to ▁improve ▁diagn osis ▁and ▁ther apy ▁by ▁detect ing ▁and ▁class ifying ▁important ▁disease ▁sub types ? ▁ ▁Emer ging ▁topics ▁The ▁first ▁wave ▁of ▁research ▁in ▁the ▁field ▁of ▁computational ▁ep igen et ics ▁was ▁driven ▁by ▁rapid ▁progress ▁of ▁experimental ▁methods ▁for ▁data ▁generation , ▁which ▁required ▁ade qu ate ▁computational ▁methods