J Med Syst - A new locally weighted K-means for cancer-aided microarray data analysis.

Tópicos

{ cancer(2502) breast(956) screen(824) }
{ spatial(1525) area(1432) region(1030) }
{ decis(3086) make(1611) patient(1517) }
{ research(1085) discuss(1038) issu(1018) }
{ data(2317) use(1299) case(1017) }
{ gene(2352) biolog(1181) express(1162) }
{ process(1125) use(805) approach(778) }
{ data(1737) use(1416) pattern(1282) }
{ treatment(1704) effect(941) patient(846) }
{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ blood(1257) pressur(1144) flow(957) }
{ model(2656) set(1616) predict(1553) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
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{ imag(1057) registr(996) error(939) }
{ method(1219) similar(1157) match(930) }
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{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
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{ activ(1138) subject(705) human(624) }
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{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

Cancer has been identified as the leading cause of death. It is predicted that around 20-26 million people will be diagnosed with cancer by 2020. With this alarming rate, there is an urgent need for a more effective methodology to understand, prevent and cure cancer. Microarray technology provides a useful basis of achieving this goal, with cluster analysis of gene expression data leading to the discrimination of patients, identification of possible tumor subtypes and individualized treatment. Amongst clustering techniques, k-means is normally chosen for its simplicity and efficiency. However, it does not account for the different importance of data attributes. This paper presents a new locally weighted extension of k-means, which has proven more accurate across many published datasets than the original and other extensions found in the literature.

Resumo Limpo

cancer identifi lead caus death predict around million peopl will diagnos cancer alarm rate urgent need effect methodolog understand prevent cure cancer microarray technolog provid use basi achiev goal cluster analysi gene express data lead discrimin patient identif possibl tumor subtyp individu treatment amongst cluster techniqu kmean normal chosen simplic effici howev account differ import data attribut paper present new local weight extens kmean proven accur across mani publish dataset origin extens found literatur

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