J. Comput. Biol. - A geometric clustering algorithm with applications to structural data.

Tópicos

{ method(1969) cluster(1462) data(1082) }
{ compound(1573) activ(1297) structur(1058) }
{ imag(1947) propos(1133) code(1026) }
{ featur(3375) classif(2383) classifi(1994) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(2830) propos(1344) filter(1198) }
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{ data(1737) use(1416) pattern(1282) }
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{ patient(1821) servic(1111) care(1106) }
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{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
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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) }
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{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

An important feature of structural data, especially those from structural determination and protein-ligand docking programs, is that their distribution could be mostly uniform. Traditional clustering algorithms developed specifically for nonuniformly distributed data may not be adequate for their classification. Here we present a geometric partitional algorithm that could be applied to both uniformly and nonuniformly distributed data. The algorithm is a top-down approach that recursively selects the outliers as the seeds to form new clusters until all the structures within a cluster satisfy a classification criterion. The algorithm has been evaluated on a diverse set of real structural data and six sets of test data. The results show that it is superior to the previous algorithms for the clustering of structural data and is similar to or better than them for the classification of the test data. The algorithm should be especially useful for the identification of the best but minor clusters and for speeding up an iterative process widely used in NMR structure determination.

Resumo Limpo

import featur structur data especi structur determin proteinligand dock program distribut most uniform tradit cluster algorithm develop specif nonuniform distribut data may adequ classif present geometr partit algorithm appli uniform nonuniform distribut data algorithm topdown approach recurs select outlier seed form new cluster structur within cluster satisfi classif criterion algorithm evalu divers set real structur data six set test data result show superior previous algorithm cluster structur data similar better classif test data algorithm especi use identif best minor cluster speed iter process wide use nmr structur determin

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