J Biomed Inform - Prioritization of potential candidate disease genes by topological similarity of protein-protein interaction network and phenotype data.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ patient(2315) diseas(1263) diabet(1191) }
{ cancer(2502) breast(956) screen(824) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2830) propos(1344) filter(1198) }
{ method(1219) similar(1157) match(930) }
{ chang(1828) time(1643) increas(1301) }
{ case(1353) use(1143) diagnosi(1136) }
{ perform(999) metric(946) measur(919) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ can(981) present(881) function(850) }
{ use(976) code(926) identifi(902) }
{ result(1111) use(1088) new(759) }
{ featur(3375) classif(2383) classifi(1994) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
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{ assess(1506) score(1403) qualiti(1306) }
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{ problem(2511) optim(1539) algorithm(950) }
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{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
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{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
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{ model(2220) cell(1177) simul(1124) }
{ general(901) number(790) one(736) }
{ search(2224) databas(1162) retriev(909) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ 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) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ 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(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

Identifying candidate disease genes is important to improve medical care. However, this task is challenging in the post-genomic era. Several computational approaches have been proposed to prioritize potential candidate genes relying on protein-protein interaction (PPI) networks. However, the experimental PPI network is usually liable to contain a number of spurious interactions. In this paper, we construct a reliable heterogeneous network by fusing multiple networks, a PPI network reconstructed by topological similarity, a phenotype similarity network and known associations between diseases and genes. We then devise a random walk-based algorithm on the reliable heterogeneous network called RWRHN to prioritize potential candidate genes for inherited diseases. The results of leave-one-out cross-validation experiments show that the RWRHN algorithm has better performance than the RWRH and CIPHER methods in inferring disease genes. Furthermore, RWRHN is used to predict novel causal genes for 16 diseases, including breast cancer, diabetes mellitus type 2, and prostate cancer, as well as to detect disease-related protein complexes. The top predictions are supported by literature evidence.

Resumo Limpo

identifi candid diseas gene import improv medic care howev task challeng postgenom era sever comput approach propos priorit potenti candid gene reli proteinprotein interact ppi network howev experiment ppi network usual liabl contain number spurious interact paper construct reliabl heterogen network fuse multipl network ppi network reconstruct topolog similar phenotyp similar network known associ diseas gene devis random walkbas algorithm reliabl heterogen network call rwrhn priorit potenti candid gene inherit diseas result leaveoneout crossvalid experi show rwrhn algorithm better perform rwrh cipher method infer diseas gene furthermor rwrhn use predict novel causal gene diseas includ breast cancer diabet mellitus type prostat cancer well detect diseaserel protein complex top predict support literatur evid

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