Brief. Bioinformatics - Gene set enrichment analysis: performance evaluation and usage guidelines.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ data(3963) clinic(1234) research(1004) }
{ network(2748) neural(1063) input(814) }
{ analysi(2126) use(1163) compon(1037) }
{ method(1969) cluster(1462) data(1082) }
{ data(1737) use(1416) pattern(1282) }
{ method(1219) similar(1157) match(930) }
{ howev(809) still(633) remain(590) }
{ model(2341) predict(2261) use(1141) }
{ can(774) often(719) complex(702) }
{ system(1976) rule(880) can(841) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ model(2656) set(1616) predict(1553) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ bind(1733) structur(1185) ligand(1036) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ studi(2440) review(1878) systemat(933) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ clinic(1479) use(1117) guidelin(835) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ spatial(1525) area(1432) region(1030) }
{ model(3480) simul(1196) paramet(876) }
{ sampl(1606) size(1419) use(1276) }
{ intervent(3218) particip(2042) group(1664) }
{ structur(1116) can(940) graph(676) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ decis(3086) make(1611) patient(1517) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(1947) propos(1133) code(1026) }
{ inform(2794) health(2639) internet(1427) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
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{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ 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) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ 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) }
{ process(1125) use(805) approach(778) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

A central goal of biology is understanding and describing the molecular basis of plasticity: the sets of genes that are combinatorially selected by exogenous and endogenous environmental changes, and the relations among the genes. The most viable current approach to this problem consists of determining whether sets of genes are connected by some common theme, e.g. genes from the same pathway are overrepresented among those whose differential expression in response to a perturbation is most pronounced. There are many approaches to this problem, and the results they produce show a fair amount of dispersion, but they all fall within a common framework consisting of a few basic components. We critically review these components, suggest best practices for carrying out each step, and propose a voting method for meeting the challenge of assessing different methods on a large number of experimental data sets in the absence of a gold standard.

Resumo Limpo

central goal biolog understand describ molecular basi plastic set gene combinatori select exogen endogen environment chang relat among gene viabl current approach problem consist determin whether set gene connect common theme eg gene pathway overrepres among whose differenti express respons perturb pronounc mani approach problem result produc show fair amount dispers fall within common framework consist basic compon critic review compon suggest best practic carri step propos vote method meet challeng assess differ method larg number experiment data set absenc gold standard

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