J Chem Inf Model - Investigation of the use of spectral clustering for the analysis of molecular data.

Tópicos

{ method(1969) cluster(1462) data(1082) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ signal(2180) analysi(812) frequenc(800) }
{ measur(2081) correl(1212) valu(896) }
{ method(984) reconstruct(947) comput(926) }
{ model(3480) simul(1196) paramet(876) }
{ imag(2830) propos(1344) filter(1198) }
{ algorithm(1844) comput(1787) effici(935) }
{ care(1570) inform(1187) nurs(1089) }
{ state(1844) use(1261) util(961) }
{ first(2504) two(1366) second(1323) }
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{ process(1125) use(805) approach(778) }
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{ featur(3375) classif(2383) classifi(1994) }
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{ imag(2675) segment(2577) method(1081) }
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{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
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{ risk(3053) factor(974) diseas(938) }
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{ import(1318) role(1303) understand(862) }
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{ visual(1396) interact(850) tool(830) }
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{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
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{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ 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) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Spectral clustering involves placing objects into clusters based on the eigenvectors and eigenvalues of an associated matrix. The technique was first applied to molecular data by Brewer [J. Chem. Inf. Model. 2007, 47, 1727-1733] who demonstrated its use on a very small dataset of 125 COX-2 inhibitors. We have determined suitable parameters for spectral clustering using a wide variety of molecular descriptors and several datasets of a few thousand compounds and compared the results of clustering using a nonoverlapping version of Brewer's use of Sarker and Boyer's algorithm with that of Ward's and k-means clustering. We then replaced the exact eigendecomposition method with two different approximate methods and concluded that Singular Value Decomposition is the most appropriate method for clustering larger compound collections of up to 100,000 compounds. We have also used spectral clustering with the Tversky coefficient to generate two sets of clusters linked by a common set of eigenvalues and have used this novel approach to cluster sets of fragments such as those used in fragment-based drug design.

Resumo Limpo

spectral cluster involv place object cluster base eigenvector eigenvalu associ matrix techniqu first appli molecular data brewer j chem inf model demonstr use small dataset cox inhibitor determin suitabl paramet spectral cluster use wide varieti molecular descriptor sever dataset thousand compound compar result cluster use nonoverlap version brewer use sarker boyer algorithm ward kmean cluster replac exact eigendecomposit method two differ approxim method conclud singular valu decomposit appropri method cluster larger compound collect compound also use spectral cluster tverski coeffici generat two set cluster link common set eigenvalu use novel approach cluster set fragment use fragmentbas drug design

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