Artif Intell Med - Integration of gene signatures using biological knowledge.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ import(1318) role(1303) understand(862) }
{ can(981) present(881) function(850) }
{ method(984) reconstruct(947) comput(926) }
{ studi(1410) differ(1259) use(1210) }
{ group(2977) signific(1463) compar(1072) }
{ case(1353) use(1143) diagnosi(1136) }
{ intervent(3218) particip(2042) group(1664) }
{ framework(1458) process(801) describ(734) }
{ perform(999) metric(946) measur(919) }
{ time(1939) patient(1703) rate(768) }
{ patient(2315) diseas(1263) diabet(1191) }
{ concept(1167) ontolog(924) domain(897) }
{ data(1714) softwar(1251) tool(1186) }
{ model(2220) cell(1177) simul(1124) }
{ state(1844) use(1261) util(961) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ measur(2081) correl(1212) valu(896) }
{ imag(2830) propos(1344) filter(1198) }
{ treatment(1704) effect(941) patient(846) }
{ problem(2511) optim(1539) algorithm(950) }
{ design(1359) user(1324) use(1319) }
{ featur(1941) imag(1645) propos(1176) }
{ data(3963) clinic(1234) research(1004) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ structur(1116) can(940) graph(676) }
{ implement(1333) system(1263) develop(1122) }
{ imag(1947) propos(1133) code(1026) }
{ imag(1057) registr(996) error(939) }
{ featur(3375) classif(2383) classifi(1994) }
{ assess(1506) score(1403) qualiti(1306) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ perform(1367) use(1326) method(1137) }
{ spatial(1525) area(1432) region(1030) }
{ signal(2180) analysi(812) frequenc(800) }
{ sampl(1606) size(1419) use(1276) }
{ first(2504) two(1366) second(1323) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ high(1669) rate(1365) level(1280) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ model(3404) distribut(989) bayesian(671) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ control(1307) perform(991) simul(935) }
{ search(2224) databas(1162) retriev(909) }
{ howev(809) still(633) remain(590) }
{ risk(3053) factor(974) diseas(938) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ data(3008) multipl(1320) sourc(1022) }
{ activ(1138) subject(705) human(624) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ 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) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

JECTIVE: Gene expression patterns that distinguish clinically significant disease subclasses may not only play a prominent role in diagnosis, but also lead to the therapeutic strategies tailoring the treatment to the particular biology of each disease. Nevertheless, gene expression signatures derived through statistical feature-extraction procedures on population datasets have received rightful criticism, since they share few genes in common, even when derived from the same dataset. We focus on knowledge complementarities conveyed by two or more gene-expression signatures by means of embedded biological processes and pathways, which alternatively form a meta-knowledge platform of analysis towards a more global, robust and powerful solution.METHODS: The main contribution of this work is the introduction and study of an approach for integrating different gene signatures based on the underlying biological knowledge, in an attempt to derive a unified global solution. It is further recognized that one group's signature does not perform well on another group's data, due to incompatibilities of microarray technologies and the experimental design. We assess this cross-platform aspect, showing that a unified solution derived on the basis of both statistical and biological validation may also help in overcoming such inconsistencies.RESULTS: Based on the proposed approach we derived a unified 69-gene signature, which outperforms significantly the performance of the initial signatures succeeding a 0.73 accuracy metric on 234 new patients with 81% sensitivity and 64% specificity. The same signature manages to reveal the two prognostic groups on an additional dataset of 286 new patients obtained through a different experimental protocol and microarray platform. Furthermore, it manages to derive two clusters in a dataset from a different platform, showing remarkable difference on both gene-expression and survival-prediction levels.

Resumo Limpo

jectiv gene express pattern distinguish clinic signific diseas subclass may play promin role diagnosi also lead therapeut strategi tailor treatment particular biolog diseas nevertheless gene express signatur deriv statist featureextract procedur popul dataset receiv right critic sinc share gene common even deriv dataset focus knowledg complementar convey two geneexpress signatur mean embed biolog process pathway altern form metaknowledg platform analysi toward global robust power solutionmethod main contribut work introduct studi approach integr differ gene signatur base under biolog knowledg attempt deriv unifi global solut recogn one group signatur perform well anoth group data due incompat microarray technolog experiment design assess crossplatform aspect show unifi solut deriv basi statist biolog valid may also help overcom inconsistenciesresult base propos approach deriv unifi gene signatur outperform signific perform initi signatur succeed accuraci metric new patient sensit specif signatur manag reveal two prognost group addit dataset new patient obtain differ experiment protocol microarray platform furthermor manag deriv two cluster dataset differ platform show remark differ geneexpress survivalpredict level

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