Comput. Biol. Med. - An effective measure for assessing the quality of biclusters.

Tópicos

{ error(1145) method(1030) estim(1020) }
{ measur(2081) correl(1212) valu(896) }
{ studi(1410) differ(1259) use(1210) }
{ imag(2675) segment(2577) method(1081) }
{ method(1557) propos(1049) approach(1037) }
{ model(2656) set(1616) predict(1553) }
{ can(981) present(881) function(850) }
{ use(1733) differ(960) four(931) }
{ data(1737) use(1416) pattern(1282) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ assess(1506) score(1403) qualiti(1306) }
{ learn(2355) train(1041) set(1003) }
{ howev(809) still(633) remain(590) }
{ risk(3053) factor(974) diseas(938) }
{ gene(2352) biolog(1181) express(1162) }
{ patient(1821) servic(1111) care(1106) }
{ structur(1116) can(940) graph(676) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
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{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ compound(1573) activ(1297) structur(1058) }
{ studi(1119) effect(1106) posit(819) }
{ state(1844) use(1261) util(961) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ data(3008) multipl(1320) sourc(1022) }
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{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ patient(2315) diseas(1263) diabet(1191) }
{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
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{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
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{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
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{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ data(3963) clinic(1234) research(1004) }
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{ system(1050) medic(1026) inform(1018) }
{ visual(1396) interact(850) tool(830) }
{ perform(1367) use(1326) method(1137) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ 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) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

Biclustering is becoming a popular technique for the study of gene expression data. This is mainly due to the capability of biclustering to address the data using various dimensions simultaneously, as opposed to clustering, which can use only one dimension at the time. Different heuristics have been proposed in order to discover interesting biclusters in data. Such heuristics have one common characteristic: they are guided by a measure that determines the quality of biclusters. It follows that defining such a measure is probably the most important aspect. One of the popular quality measure is the mean squared residue (MSR). However, it has been proven that MSR fails at identifying some kind of patterns. This motivates us to introduce a novel measure, called virtual error (VE), that overcomes this limitation. Results obtained by using VE confirm that it can identify interesting patterns that could not be found by MSR.

Resumo Limpo

biclust becom popular techniqu studi gene express data main due capabl biclust address data use various dimens simultan oppos cluster can use one dimens time differ heurist propos order discov interest biclust data heurist one common characterist guid measur determin qualiti biclust follow defin measur probabl import aspect one popular qualiti measur mean squar residu msr howev proven msr fail identifi kind pattern motiv us introduc novel measur call virtual error ve overcom limit result obtain use ve confirm can identifi interest pattern found msr

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