Methods Inf Med - Supporting regenerative medicine by integrative dimensionality reduction.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ model(2656) set(1616) predict(1553) }
{ compound(1573) activ(1297) structur(1058) }
{ learn(2355) train(1041) set(1003) }
{ data(3008) multipl(1320) sourc(1022) }
{ cost(1906) reduc(1198) effect(832) }
{ method(1557) propos(1049) approach(1037) }
{ first(2504) two(1366) second(1323) }
{ search(2224) databas(1162) retriev(909) }
{ perform(1367) use(1326) method(1137) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ use(1733) differ(960) four(931) }
{ method(2212) result(1239) propos(1039) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ health(3367) inform(1360) care(1135) }
{ patient(1821) servic(1111) care(1106) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ decis(3086) make(1611) patient(1517) }
{ system(1976) rule(880) can(841) }
{ imag(1057) registr(996) error(939) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ clinic(1479) use(1117) guidelin(835) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ record(1888) medic(1808) patient(1693) }
{ research(1218) medic(880) student(794) }
{ intervent(3218) particip(2042) group(1664) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ drug(1928) target(777) effect(648) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ measur(2081) correl(1212) valu(896) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ 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) }
{ chang(1828) time(1643) increas(1301) }
{ concept(1167) ontolog(924) domain(897) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ 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) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ visual(1396) interact(850) tool(830) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ 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) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ 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) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

JECTIVE: The assessment of the developmental potential of stem cells is a crucial step towards their clinical application in regenerative medicine. It has been demonstrated that genome-wide expression profiles can predict the cellular differentiation stage by means of dimensionality reduction methods. Here we show that these techniques can be further strengthened to support decision making with i) a novel strategy for gene selection; ii) methods for combining the evidence from multiple data sets.METHODS: We propose to exploit dimensionality reduction methods for the selection of genes specifically activated in different stages of differentiation. To obtain an integrated predictive model, the expression values of the selected genes from multiple data sets are combined. We investigated distinct approaches that either aggregate data sets or use learning ensembles.RESULTS: We analyzed the performance of the proposed methods on six publicly available data sets. The selection procedure identified a reduced subset of genes whose expression values gave rise to an accurate stage prediction. The assessment of predictive accuracy demonstrated a high quality of predictions for most of the data integration methods presented.CONCLUSION: The experimental results highlighted the main potentials of proposed approaches. These include the ability to predict the true staging by combining multiple training data sets when this could not be inferred from a single data source, and to focus the analysis on a reduced list of genes of similar predictive performance.

Resumo Limpo

jectiv assess development potenti stem cell crucial step toward clinic applic regen medicin demonstr genomewid express profil can predict cellular differenti stage mean dimension reduct method show techniqu can strengthen support decis make novel strategi gene select ii method combin evid multipl data setsmethod propos exploit dimension reduct method select gene specif activ differ stage differenti obtain integr predict model express valu select gene multipl data set combin investig distinct approach either aggreg data set use learn ensemblesresult analyz perform propos method six public avail data set select procedur identifi reduc subset gene whose express valu gave rise accur stage predict assess predict accuraci demonstr high qualiti predict data integr method presentedconclus experiment result highlight main potenti propos approach includ abil predict true stage combin multipl train data set infer singl data sourc focus analysi reduc list gene similar predict perform

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