Comput Methods Programs Biomed - The classification of cancer stage microarray data.

Tópicos

{ cancer(2502) breast(956) screen(824) }
{ featur(3375) classif(2383) classifi(1994) }
{ perform(1367) use(1326) method(1137) }
{ gene(2352) biolog(1181) express(1162) }
{ model(3404) distribut(989) bayesian(671) }
{ model(2341) predict(2261) use(1141) }
{ data(3008) multipl(1320) sourc(1022) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ learn(2355) train(1041) set(1003) }
{ model(2220) cell(1177) simul(1124) }
{ bind(1733) structur(1185) ligand(1036) }
{ take(945) account(800) differ(722) }
{ assess(1506) score(1403) qualiti(1306) }
{ monitor(1329) mobil(1314) devic(1160) }
{ result(1111) use(1088) new(759) }
{ method(2212) result(1239) propos(1039) }
{ framework(1458) process(801) describ(734) }
{ control(1307) perform(991) simul(935) }
{ care(1570) inform(1187) nurs(1089) }
{ case(1353) use(1143) diagnosi(1136) }
{ visual(1396) interact(850) tool(830) }
{ ehr(2073) health(1662) electron(1139) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ use(1733) differ(960) four(931) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ state(1844) use(1261) util(961) }
{ 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) }
{ signal(2180) analysi(812) frequenc(800) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ 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) }
{ use(976) code(926) identifi(902) }
{ 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

Correctly diagnosing the cancer stage is most important for selecting an appropriate cancer treatment option for a patient. Recent advances in microarray technology allow the cancer stage to be predicted using gene expression patterns. The cancer stage is in ordinal scale. In this paper, we employ strict ordinal regressions including cumulative logit model in traditional statistics with data dimensionality reduction, and distribution free approaches of large margin rank boundaries implemented by the support vector machine, as well as an ensemble ranking scheme to model the cancer stage using gene expression microarray data. Predictive genes included in models are selected by univariate feature ranking, and recursive feature elimination. We perform cross-validation experiments to assess and compare classification accuracies of ordinal and non-ordinal algorithms on five cancer stage microarray datasets. We conclude that a strict ordinal classifier trained by a validated approach can predict the cancer stage more accurately than traditional non-ordinal classifiers without considering the order of cancer stages.

Resumo Limpo

correct diagnos cancer stage import select appropri cancer treatment option patient recent advanc microarray technolog allow cancer stage predict use gene express pattern cancer stage ordin scale paper employ strict ordin regress includ cumul logit model tradit statist data dimension reduct distribut free approach larg margin rank boundari implement support vector machin well ensembl rank scheme model cancer stage use gene express microarray data predict gene includ model select univari featur rank recurs featur elimin perform crossvalid experi assess compar classif accuraci ordin nonordin algorithm five cancer stage microarray dataset conclud strict ordin classifi train valid approach can predict cancer stage accur tradit nonordin classifi without consid order cancer stage

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