J. Comput. Biol. - Biomarker discovery using statistically significant gene sets.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ featur(3375) classif(2383) classifi(1994) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ can(774) often(719) complex(702) }
{ method(1557) propos(1049) approach(1037) }
{ age(1611) year(1155) adult(843) }
{ drug(1928) target(777) effect(648) }
{ case(1353) use(1143) diagnosi(1136) }
{ model(2656) set(1616) predict(1553) }
{ howev(809) still(633) remain(590) }
{ activ(1138) subject(705) human(624) }
{ imag(1947) propos(1133) code(1026) }
{ system(1976) rule(880) can(841) }
{ error(1145) method(1030) estim(1020) }
{ algorithm(1844) comput(1787) effici(935) }
{ search(2224) databas(1162) retriev(909) }
{ risk(3053) factor(974) diseas(938) }
{ group(2977) signific(1463) compar(1072) }
{ use(2086) technolog(871) perceiv(783) }
{ method(2212) result(1239) propos(1039) }
{ model(3404) distribut(989) bayesian(671) }
{ framework(1458) process(801) describ(734) }
{ design(1359) user(1324) use(1319) }
{ care(1570) inform(1187) nurs(1089) }
{ perform(999) metric(946) measur(919) }
{ health(3367) inform(1360) care(1135) }
{ medic(1828) order(1363) alert(1069) }
{ sampl(1606) size(1419) use(1276) }
{ first(2504) two(1366) second(1323) }
{ detect(2391) sensit(1101) algorithm(908) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ 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) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ problem(2511) optim(1539) algorithm(950) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ 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) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ 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) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ 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) }

Resumo

Analysis of large gene expression data sets in the presence and absence of a phenotype can lead to the selection of a group of genes serving as biomarkers jointly predicting the phenotype. Among gene selection methods, filter methods derived from ranked individual genes have been widely used in existing products for diagnosis and prognosis. Univariate filter approaches selecting genes individually, although computationally efficient, often ignore gene interactions inherent in the biological data. On the other hand, multivariate approaches selecting gene subsets are known to have a higher risk of selecting spurious gene subsets due to the overfitting of the vast number of gene subsets evaluated. Here we propose a framework of statistical significance tests for multivariate feature selection that can reduce the risk of selecting spurious gene subsets. Using three existing data sets, we show that our proposed approach is an essential step to identify such a gene set that is generated by a significant interaction of its members, even improving classification performance when compared to established approaches. This technique can be applied for the discovery of robust biomarkers for medical diagnosis.

Resumo Limpo

analysi larg gene express data set presenc absenc phenotyp can lead select group gene serv biomark joint predict phenotyp among gene select method filter method deriv rank individu gene wide use exist product diagnosi prognosi univari filter approach select gene individu although comput effici often ignor gene interact inher biolog data hand multivari approach select gene subset known higher risk select spurious gene subset due overfit vast number gene subset evalu propos framework statist signific test multivari featur select can reduc risk select spurious gene subset use three exist data set show propos approach essenti step identifi gene set generat signific interact member even improv classif perform compar establish approach techniqu can appli discoveri robust biomark medic diagnosi

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