J Integr Bioinform - Network expansion and pathway enrichment analysis towards biologically significant findings from microarrays.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ import(1318) role(1303) understand(862) }
{ method(1219) similar(1157) match(930) }
{ patient(2315) diseas(1263) diabet(1191) }
{ general(901) number(790) one(736) }
{ perform(999) metric(946) measur(919) }
{ health(3367) inform(1360) care(1135) }
{ group(2977) signific(1463) compar(1072) }
{ can(774) often(719) complex(702) }
{ chang(1828) time(1643) increas(1301) }
{ concept(1167) ontolog(924) domain(897) }
{ method(1557) propos(1049) approach(1037) }
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{ use(2086) technolog(871) perceiv(783) }
{ estim(2440) model(1874) function(577) }
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{ data(3963) clinic(1234) research(1004) }
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{ data(1714) softwar(1251) tool(1186) }
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{ case(1353) use(1143) diagnosi(1136) }
{ blood(1257) pressur(1144) flow(957) }
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{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
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{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
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{ high(1669) rate(1365) level(1280) }
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{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
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{ activ(1452) weight(1219) physic(1104) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In many cases, crucial genes show relatively slight changes between groups of samples (e.g. normal vs. disease), and many genes selected from microarray differential analysis by measuring the expression level statistically are also poorly annotated and lack of biological significance. In this paper, we present an innovative approach - network expansion and pathway enrichment analysis (NEPEA) for integrative microarray analysis. We assume that organized knowledge will help microarray data analysis in significant ways, and the organized knowledge could be represented as molecular interaction networks or biological pathways. Based on this hypothesis, we develop the NEPEA framework based on network expansion from the human annotated and predicted protein interaction (HAPPI) database, and pathway enrichment from the human pathway database (HPD). We use a recently-published microarray dataset (GSE24215) related to insulin resistance and type 2 diabetes (T2D) as case study, since this study provided a thorough experimental validation for both genes and pathways identified computationally from classical microarray analysis and pathway analysis. We perform our NEPEA analysis for this dataset based on the results from the classical microarray analysis to identify biologically significant genes and pathways. Our findings are not only consistent with the original findings mostly, but also obtained more supports from other literatures.

Resumo Limpo

mani case crucial gene show relat slight chang group sampl eg normal vs diseas mani gene select microarray differenti analysi measur express level statist also poor annot lack biolog signific paper present innov approach network expans pathway enrich analysi nepea integr microarray analysi assum organ knowledg will help microarray data analysi signific way organ knowledg repres molecular interact network biolog pathway base hypothesi develop nepea framework base network expans human annot predict protein interact happi databas pathway enrich human pathway databas hpd use recentlypublish microarray dataset gse relat insulin resist type diabet td case studi sinc studi provid thorough experiment valid gene pathway identifi comput classic microarray analysi pathway analysi perform nepea analysi dataset base result classic microarray analysi identifi biolog signific gene pathway find consist origin find most also obtain support literatur

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