J. Comput. Biol. - Probabilistic cellular automata.

Tópicos

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{ take(945) account(800) differ(722) }
{ model(2220) cell(1177) simul(1124) }
{ visual(1396) interact(850) tool(830) }
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{ system(1976) rule(880) can(841) }
{ problem(2511) optim(1539) algorithm(950) }
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{ imag(1057) registr(996) error(939) }
{ treatment(1704) effect(941) patient(846) }
{ framework(1458) process(801) describ(734) }
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{ research(1085) discuss(1038) issu(1018) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ use(2086) technolog(871) perceiv(783) }
{ use(1733) differ(960) four(931) }
{ activ(1452) weight(1219) physic(1104) }
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{ error(1145) method(1030) estim(1020) }
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{ algorithm(1844) comput(1787) effici(935) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
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{ patient(2837) hospit(1953) medic(668) }
{ signal(2180) analysi(812) frequenc(800) }
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{ concept(1167) ontolog(924) domain(897) }
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{ control(1307) perform(991) simul(935) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ compound(1573) activ(1297) structur(1058) }
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{ studi(1119) effect(1106) posit(819) }
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{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
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{ monitor(1329) mobil(1314) devic(1160) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
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{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

Cellular automata are binary lattices used for modeling complex dynamical systems. The automaton evolves iteratively from one configuration to another, using some local transition rule based on the number of ones in the neighborhood of each cell. With respect to the number of cells allowed to change per iteration, we speak of either synchronous or asynchronous automata. If randomness is involved to some degree in the transition rule, we speak of probabilistic automata, otherwise they are called deterministic. With either type of cellular automaton we are dealing with, the main theoretical challenge stays the same: starting from an arbitrary initial configuration, predict (with highest accuracy) the end configuration. If the automaton is deterministic, the outcome simplifies to one of two configurations, all zeros or all ones. If the automaton is probabilistic, the whole process is modeled by a finite homogeneous Markov chain, and the outcome is the corresponding stationary distribution. Based on our previous results for the asynchronous case-connecting the probability of a configuration in the stationary distribution to its number of zero-one borders-the article offers both numerical and theoretical insight into the long-term behavior of synchronous cellular automata.

Resumo Limpo

cellular automata binari lattic use model complex dynam system automaton evolv iter one configur anoth use local transit rule base number one neighborhood cell respect number cell allow chang per iter speak either synchron asynchron automata random involv degre transit rule speak probabilist automata otherwis call determinist either type cellular automaton deal main theoret challeng stay start arbitrari initi configur predict highest accuraci end configur automaton determinist outcom simplifi one two configur zero one automaton probabilist whole process model finit homogen markov chain outcom correspond stationari distribut base previous result asynchron caseconnect probabl configur stationari distribut number zeroon bordersth articl offer numer theoret insight longterm behavior synchron cellular automata

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