Comput Math Methods Med - Multiple suboptimal solutions for prediction rules in gene expression data.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ learn(2355) train(1041) set(1003) }
{ featur(3375) classif(2383) classifi(1994) }
{ howev(809) still(633) remain(590) }
{ method(1219) similar(1157) match(930) }
{ design(1359) user(1324) use(1319) }
{ risk(3053) factor(974) diseas(938) }
{ research(1085) discuss(1038) issu(1018) }
{ detect(2391) sensit(1101) algorithm(908) }
{ measur(2081) correl(1212) valu(896) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ studi(1119) effect(1106) posit(819) }
{ data(2317) use(1299) case(1017) }
{ can(774) often(719) complex(702) }
{ take(945) account(800) differ(722) }
{ model(3480) simul(1196) paramet(876) }
{ sampl(1606) size(1419) use(1276) }
{ cancer(2502) breast(956) screen(824) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ system(1976) rule(880) can(841) }
{ network(2748) neural(1063) input(814) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ framework(1458) process(801) describ(734) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ featur(1941) imag(1645) propos(1176) }
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{ compound(1573) activ(1297) structur(1058) }
{ state(1844) use(1261) util(961) }
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{ analysi(2126) use(1163) compon(1037) }
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{ inform(2794) health(2639) internet(1427) }
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{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
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{ data(3963) clinic(1234) research(1004) }
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{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ visual(1396) interact(850) tool(830) }
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{ blood(1257) pressur(1144) flow(957) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ 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) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ 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) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }

Resumo

This paper discusses mathematical and statistical aspects in analysis methods applied to microarray gene expressions. We focus on pattern recognition to extract informative features embedded in the data for prediction of phenotypes. It has been pointed out that there are severely difficult problems due to the unbalance in the number of observed genes compared with the number of observed subjects. We make a reanalysis of microarray gene expression published data to detect many other gene sets with almost the same performance. We conclude in the current stage that it is not possible to extract only informative genes with high performance in the all observed genes. We investigate the reason why this difficulty still exists even though there are actively proposed analysis methods and learning algorithms in statistical machine learning approaches. We focus on the mutual coherence or the absolute value of the Pearson correlations between two genes and describe the distributions of the correlation for the selected set of genes and the total set. We show that the problem of finding informative genes in high dimensional data is ill-posed and that the difficulty is closely related with the mutual coherence.

Resumo Limpo

paper discuss mathemat statist aspect analysi method appli microarray gene express focus pattern recognit extract inform featur embed data predict phenotyp point sever difficult problem due unbal number observ gene compar number observ subject make reanalysi microarray gene express publish data detect mani gene set almost perform conclud current stage possibl extract inform gene high perform observ gene investig reason difficulti still exist even though activ propos analysi method learn algorithm statist machin learn approach focus mutual coher absolut valu pearson correl two gene describ distribut correl select set gene total set show problem find inform gene high dimension data illpos difficulti close relat mutual coher

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