Comput. Biol. Med. - Multi-stage filtering for improving confidence level and determining dominant clusters in clustering algorithms of gene expression data.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ method(1969) cluster(1462) data(1082) }
{ system(1976) rule(880) can(841) }
{ analysi(2126) use(1163) compon(1037) }
{ framework(1458) process(801) describ(734) }
{ age(1611) year(1155) adult(843) }
{ use(2086) technolog(871) perceiv(783) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ error(1145) method(1030) estim(1020) }
{ method(1557) propos(1049) approach(1037) }
{ howev(809) still(633) remain(590) }
{ model(2341) predict(2261) use(1141) }
{ state(1844) use(1261) util(961) }
{ high(1669) rate(1365) level(1280) }
{ drug(1928) target(777) effect(648) }
{ imag(1947) propos(1133) code(1026) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ studi(2440) review(1878) systemat(933) }
{ chang(1828) time(1643) increas(1301) }
{ extract(1171) text(1153) clinic(932) }
{ case(1353) use(1143) diagnosi(1136) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
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{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
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{ monitor(1329) mobil(1314) devic(1160) }
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{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
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{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
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{ data(3008) multipl(1320) sourc(1022) }
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{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ use(976) code(926) identifi(902) }
{ implement(1333) system(1263) develop(1122) }
{ 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

A drastic improvement in the analysis of gene expression has lead to new discoveries in bioinformatics research. In order to analyse the gene expression data, fuzzy clustering algorithms are widely used. However, the resulting analyses from these specific types of algorithms may lead to confusion in hypotheses with regard to the suggestion of dominant function for genes of interest. Besides that, the current fuzzy clustering algorithms do not conduct a thorough analysis of genes with low membership values. Therefore, we present a novel computational framework called the "multi-stage filtering-Clustering Functional Annotation" (msf-CluFA) for clustering gene expression data. The framework consists of four components: fuzzy c-means clustering (msf-CluFA-0), achieving dominant cluster (msf-CluFA-1), improving confidence level (msf-CluFA-2) and combination of msf-CluFA-0, msf-CluFA-1 and msf-CluFA-2 (msf-CluFA-3). By employing double filtering in msf-CluFA-1 and apriori algorithms in msf-CluFA-2, our new framework is capable of determining the dominant clusters and improving the confidence level of genes with lower membership values by means of which the unknown genes can be predicted.

Resumo Limpo

drastic improv analysi gene express lead new discoveri bioinformat research order analys gene express data fuzzi cluster algorithm wide use howev result analys specif type algorithm may lead confus hypothes regard suggest domin function gene interest besid current fuzzi cluster algorithm conduct thorough analysi gene low membership valu therefor present novel comput framework call multistag filteringclust function annot msfclufa cluster gene express data framework consist four compon fuzzi cmean cluster msfclufa achiev domin cluster msfclufa improv confid level msfclufa combin msfclufa msfclufa msfclufa msfclufa employ doubl filter msfclufa apriori algorithm msfclufa new framework capabl determin domin cluster improv confid level gene lower membership valu mean unknown gene can predict

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