Brief. Bioinformatics - How to cluster gene expression dynamics in response to environmental signals.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ model(2220) cell(1177) simul(1124) }
{ model(3404) distribut(989) bayesian(671) }
{ research(1085) discuss(1038) issu(1018) }
{ blood(1257) pressur(1144) flow(957) }
{ control(1307) perform(991) simul(935) }
{ compound(1573) activ(1297) structur(1058) }
{ spatial(1525) area(1432) region(1030) }
{ signal(2180) analysi(812) frequenc(800) }
{ sequenc(1873) structur(1644) protein(1328) }
{ model(2656) set(1616) predict(1553) }
{ activ(1138) subject(705) human(624) }
{ can(774) often(719) complex(702) }
{ imag(1057) registr(996) error(939) }
{ method(1219) similar(1157) match(930) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ learn(2355) train(1041) set(1003) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ import(1318) role(1303) understand(862) }
{ model(3480) simul(1196) paramet(876) }
{ patient(2837) hospit(1953) medic(668) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
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{ visual(1396) interact(850) tool(830) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ 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) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Organisms usually cope with change in the environment by altering the dynamic trajectory of gene expression to adjust the complement of active proteins. The identification of particular sets of genes whose expression is adaptive in response to environmental changes helps to understand the mechanistic base of gene-environment interactions essential for organismic development. We describe a computational framework for clustering the dynamics of gene expression in distinct environments through Gaussian mixture fitting to the expression data measured at a set of discrete time points. We outline a number of quantitative testable hypotheses about the patterns of dynamic gene expression in changing environments and gene-environment interactions causing developmental differentiation. The future directions of gene clustering in terms of incorporations of the latest biological discoveries and statistical innovations are discussed. We provide a set of computational tools that are applicable to modeling and analysis of dynamic gene expression data measured in multiple environments.

Resumo Limpo

organ usual cope chang environ alter dynam trajectori gene express adjust complement activ protein identif particular set gene whose express adapt respons environment chang help understand mechanist base geneenviron interact essenti organism develop describ comput framework cluster dynam gene express distinct environ gaussian mixtur fit express data measur set discret time point outlin number quantit testabl hypothes pattern dynam gene express chang environ geneenviron interact caus development differenti futur direct gene cluster term incorpor latest biolog discoveri statist innov discuss provid set comput tool applic model analysi dynam gene express data measur multipl environ

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