Comput. Biol. Med. - Gene expression microarray classification using PCA-BEL.

Tópicos

{ featur(3375) classif(2383) classifi(1994) }
{ analysi(2126) use(1163) compon(1037) }
{ cancer(2502) breast(956) screen(824) }
{ can(774) often(719) complex(702) }
{ method(1557) propos(1049) approach(1037) }
{ model(2220) cell(1177) simul(1124) }
{ high(1669) rate(1365) level(1280) }
{ network(2748) neural(1063) input(814) }
{ extract(1171) text(1153) clinic(932) }
{ imag(2675) segment(2577) method(1081) }
{ problem(2511) optim(1539) algorithm(950) }
{ algorithm(1844) comput(1787) effici(935) }
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{ method(2212) result(1239) propos(1039) }
{ imag(1057) registr(996) error(939) }
{ imag(2830) propos(1344) filter(1198) }
{ take(945) account(800) differ(722) }
{ assess(1506) score(1403) qualiti(1306) }
{ framework(1458) process(801) describ(734) }
{ learn(2355) train(1041) set(1003) }
{ search(2224) databas(1162) retriev(909) }
{ compound(1573) activ(1297) structur(1058) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ model(2656) set(1616) predict(1553) }
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{ activ(1138) subject(705) human(624) }
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{ research(1218) medic(880) student(794) }
{ 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) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
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{ 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

In this paper, a novel hybrid method is proposed based on Principal Component Analysis (PCA) and Brain Emotional Learning (BEL) network for the classification tasks of gene-expression microarray data. BEL network is a computational neural model of the emotional brain which simulates its neuropsychological features. The distinctive feature of BEL is its low computational complexity which makes it suitable for high dimensional feature vector classification. Thus BEL can be adopted in pattern recognition in order to overcome the curse of dimensionality problem. In the experimental studies, the proposed model is utilized for the classification problems of the small round blue cell tumors (SRBCTs), high grade gliomas (HGG), lung, colon and breast cancer datasets. According to the results based on 5-fold cross validation, the PCA-BEL provides an average accuracy of 100%, 96%, 98.32%, 87.40% and 88% in these datasets respectively. Therefore, they can be effectively used in gene-expression microarray classification tasks.

Resumo Limpo

paper novel hybrid method propos base princip compon analysi pca brain emot learn bel network classif task geneexpress microarray data bel network comput neural model emot brain simul neuropsycholog featur distinct featur bel low comput complex make suitabl high dimension featur vector classif thus bel can adopt pattern recognit order overcom curs dimension problem experiment studi propos model util classif problem small round blue cell tumor srbcts high grade glioma hgg lung colon breast cancer dataset accord result base fold cross valid pcabel provid averag accuraci dataset respect therefor can effect use geneexpress microarray classif task

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