Comput. Biol. Med. - A sub-space greedy search method for efficient Bayesian Network inference.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ method(1219) similar(1157) match(930) }
{ algorithm(1844) comput(1787) effici(935) }
{ search(2224) databas(1162) retriev(909) }
{ howev(809) still(633) remain(590) }
{ assess(1506) score(1403) qualiti(1306) }
{ control(1307) perform(991) simul(935) }
{ state(1844) use(1261) util(961) }
{ method(2212) result(1239) propos(1039) }
{ model(3404) distribut(989) bayesian(671) }
{ method(1557) propos(1049) approach(1037) }
{ research(1085) discuss(1038) issu(1018) }
{ model(3480) simul(1196) paramet(876) }
{ data(2317) use(1299) case(1017) }
{ cost(1906) reduc(1198) effect(832) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ measur(2081) correl(1212) valu(896) }
{ featur(3375) classif(2383) classifi(1994) }
{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
{ data(3963) clinic(1234) research(1004) }
{ compound(1573) activ(1297) structur(1058) }
{ model(2656) set(1616) predict(1553) }
{ use(2086) technolog(871) perceiv(783) }
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{ activ(1452) weight(1219) physic(1104) }
{ imag(1947) propos(1133) code(1026) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
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{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
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{ monitor(1329) mobil(1314) devic(1160) }
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{ patient(2837) hospit(1953) medic(668) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ 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) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ 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) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Bayesian network (BN) has been successfully used to infer the regulatory relationships of genes from microarray dataset. However, one major limitation of BN approach is the computational cost because the calculation time grows more than exponentially with the dimension of the dataset. In this paper, we propose a sub-space greedy search method for efficient Bayesian Network inference. Particularly, this method limits the greedy search space by only selecting gene pairs with higher partial correlation coefficients. Using both synthetic and real data, we demonstrate that the proposed method achieved comparable results with standard greedy search method yet saved ~50% of the computational time. We believe that sub-space search method can be widely used for efficient BN inference in systems biology.

Resumo Limpo

bayesian network bn success use infer regulatori relationship gene microarray dataset howev one major limit bn approach comput cost calcul time grow exponenti dimens dataset paper propos subspac greedi search method effici bayesian network infer particular method limit greedi search space select gene pair higher partial correl coeffici use synthet real data demonstr propos method achiev compar result standard greedi search method yet save comput time believ subspac search method can wide use effici bn infer system biolog

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