J Integr Bioinform - An evolutionary and visual framework for clustering of DNA microarray data.

Tópicos

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{ visual(1396) interact(850) tool(830) }
{ method(2212) result(1239) propos(1039) }
{ framework(1458) process(801) describ(734) }
{ general(901) number(790) one(736) }
{ data(3963) clinic(1234) research(1004) }
{ implement(1333) system(1263) develop(1122) }
{ can(774) often(719) complex(702) }
{ measur(2081) correl(1212) valu(896) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ method(1557) propos(1049) approach(1037) }
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{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
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{ model(3480) simul(1196) paramet(876) }
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{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ 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) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ 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) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
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{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

This paper presents a case study to show the competence of our evolutionary and visual framework for cluster analysis of DNA microarray data. The proposed framework joins a genetic algorithm for hierarchical clustering with a set of visual components of cluster tasks given by a tool. The cluster visualization tool allows us to display different views of clustering results as a means of cluster visual validation. The results of the genetic algorithm for clustering have shown that it can find better solutions than the other methods for the selected data set. Thus, this shows the reliability of the proposed framework.

Resumo Limpo

paper present case studi show compet evolutionari visual framework cluster analysi dna microarray data propos framework join genet algorithm hierarch cluster set visual compon cluster task given tool cluster visual tool allow us display differ view cluster result mean cluster visual valid result genet algorithm cluster shown can find better solut method select data set thus show reliabl propos framework

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