J Chem Inf Model - Consensus methods for combining multiple clusterings of chemical structures.

Tópicos

{ method(1969) cluster(1462) data(1082) }
{ compound(1573) activ(1297) structur(1058) }
{ studi(2440) review(1878) systemat(933) }
{ result(1111) use(1088) new(759) }
{ measur(2081) correl(1212) valu(896) }
{ data(3008) multipl(1320) sourc(1022) }
{ featur(3375) classif(2383) classifi(1994) }
{ assess(1506) score(1403) qualiti(1306) }
{ problem(2511) optim(1539) algorithm(950) }
{ treatment(1704) effect(941) patient(846) }
{ extract(1171) text(1153) clinic(932) }
{ perform(1367) use(1326) method(1137) }
{ research(1218) medic(880) student(794) }
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{ data(2317) use(1299) case(1017) }
{ cost(1906) reduc(1198) effect(832) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ can(981) present(881) function(850) }
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{ implement(1333) system(1263) develop(1122) }
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{ studi(1119) effect(1106) posit(819) }
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{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
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{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ 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) }
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{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ 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(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

The goal of consensus clustering methods is to find a consensus partition that optimally summarizes an ensemble and improves the quality of clustering compared with single clustering algorithms. In this paper, an enhanced voting-based consensus method was introduced and compared with other consensus clustering methods, including co-association-based, graph-based, and voting-based consensus methods. The MDDR and MUV data sets were used for the experiments and were represented by three 2D fingerprints: ALOGP, ECFP_4, and ECFC_4. The results were evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster using four criteria: F-measure, Quality Partition Index (QPI), Rand Index (RI), and Fowlkes-Mallows Index (FMI). The experiments suggest that the consensus methods can deliver significant improvements for the effectiveness of chemical structures clustering.

Resumo Limpo

goal consensus cluster method find consensus partit optim summar ensembl improv qualiti cluster compar singl cluster algorithm paper enhanc votingbas consensus method introduc compar consensus cluster method includ coassociationbas graphbas votingbas consensus method mddr muv data set use experi repres three d fingerprint alogp ecfp ecfc result evalu base abil cluster method separ activ inact molecul cluster use four criteria fmeasur qualiti partit index qpi rand index ri fowlkesmallow index fmi experi suggest consensus method can deliv signific improv effect chemic structur cluster

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