J. Comput. Biol. - A stationary wavelet entropy-based clustering approach accurately predicts gene expression.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ method(1969) cluster(1462) data(1082) }
{ sampl(1606) size(1419) use(1276) }
{ process(1125) use(805) approach(778) }
{ measur(2081) correl(1212) valu(896) }
{ patient(2315) diseas(1263) diabet(1191) }
{ signal(2180) analysi(812) frequenc(800) }
{ can(774) often(719) complex(702) }
{ perform(1367) use(1326) method(1137) }
{ treatment(1704) effect(941) patient(846) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ import(1318) role(1303) understand(862) }
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{ model(2656) set(1616) predict(1553) }
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{ activ(1452) weight(1219) physic(1104) }
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{ data(2317) use(1299) case(1017) }
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{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
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{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
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{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }

Resumo

Studying epigenetic landscapes is important to understand the condition for gene regulation. Clustering is a useful approach to study epigenetic landscapes by grouping genes based on their epigenetic conditions. However, classical clustering approaches that often use a representative value of the signals in a fixed-sized window do not fully use the information written in the epigenetic landscapes. Clustering approaches to maximize the information of the epigenetic signals are necessary for better understanding gene regulatory environments. For effective clustering of multidimensional epigenetic signals, we developed a method called Dewer, which uses the entropy of stationary wavelet of epigenetic signals inside enriched regions for gene clustering. Interestingly, the gene expression levels were highly correlated with the entropy levels of epigenetic signals. Dewer separates genes better than a window-based approach in the assessment using gene expression and achieved a correlation coefficient above 0.9 without using any training procedure. Our results show that the changes of the epigenetic signals are useful to study gene regulation.

Resumo Limpo

studi epigenet landscap import understand condit gene regul cluster use approach studi epigenet landscap group gene base epigenet condit howev classic cluster approach often use repres valu signal fixeds window fulli use inform written epigenet landscap cluster approach maxim inform epigenet signal necessari better understand gene regulatori environ effect cluster multidimension epigenet signal develop method call dewer use entropi stationari wavelet epigenet signal insid enrich region gene cluster interest gene express level high correl entropi level epigenet signal dewer separ gene better windowbas approach assess use gene express achiev correl coeffici without use train procedur result show chang epigenet signal use studi gene regul

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