J Biomed Inform - Comparative analysis of a novel disease phenotype network based on clinical manifestations.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ data(2317) use(1299) case(1017) }
{ extract(1171) text(1153) clinic(932) }
{ method(1969) cluster(1462) data(1082) }
{ clinic(1479) use(1117) guidelin(835) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(999) metric(946) measur(919) }
{ can(774) often(719) complex(702) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ take(945) account(800) differ(722) }
{ howev(809) still(633) remain(590) }
{ spatial(1525) area(1432) region(1030) }
{ model(3480) simul(1196) paramet(876) }
{ sampl(1606) size(1419) use(1276) }
{ structur(1116) can(940) graph(676) }
{ result(1111) use(1088) new(759) }
{ method(2212) result(1239) propos(1039) }
{ inform(2794) health(2639) internet(1427) }
{ imag(2830) propos(1344) filter(1198) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ concept(1167) ontolog(924) domain(897) }
{ care(1570) inform(1187) nurs(1089) }
{ studi(1410) differ(1259) use(1210) }
{ research(1085) discuss(1038) issu(1018) }
{ perform(1367) use(1326) method(1137) }
{ health(3367) inform(1360) care(1135) }
{ age(1611) year(1155) adult(843) }
{ cost(1906) reduc(1198) effect(832) }
{ first(2504) two(1366) second(1323) }
{ health(1844) social(1437) communiti(874) }
{ use(976) code(926) identifi(902) }
{ activ(1452) weight(1219) physic(1104) }
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{ patient(2315) diseas(1263) diabet(1191) }
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{ assess(1506) score(1403) qualiti(1306) }
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{ framework(1458) process(801) describ(734) }
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{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
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{ risk(3053) factor(974) diseas(938) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
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{ visual(1396) interact(850) tool(830) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ 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) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Systems approaches to analyzing disease phenotype networks in combination with protein functional interaction networks have great potential in illuminating disease pathophysiological mechanisms. While many genetic networks are readily available, disease phenotype networks remain largely incomplete. In this study, we built a large-scale Disease Manifestation Network (DMN) from 50,543 highly accurate disease-manifestation semantic relationships in the United Medical Language System (UMLS). Our new phenotype network contains 2305 nodes and 373,527 weighted edges to represent the disease phenotypic similarities. We first compared DMN with the networks representing genetic relationships among diseases, and demonstrated that the phenotype clustering in DMN reflects common disease genetics. Then we compared DMN with a widely-used disease phenotype network in previous gene discovery studies, called mimMiner, which was extracted from the textual descriptions in Online Mendelian Inheritance in Man (OMIM). We demonstrated that DMN contains different knowledge from the existing phenotype data source. Finally, a case study on Marfan syndrome further proved that DMN contains useful information and can provide leads to discover unknown disease causes. Integrating DMN in systems approaches with mimMiner and other data offers the opportunities to predict novel disease genetics. We made DMN publicly available at nlp/case.edu/public/data/DMN.

Resumo Limpo

system approach analyz diseas phenotyp network combin protein function interact network great potenti illumin diseas pathophysiolog mechan mani genet network readili avail diseas phenotyp network remain larg incomplet studi built largescal diseas manifest network dmn high accur diseasemanifest semant relationship unit medic languag system uml new phenotyp network contain node weight edg repres diseas phenotyp similar first compar dmn network repres genet relationship among diseas demonstr phenotyp cluster dmn reflect common diseas genet compar dmn widelyus diseas phenotyp network previous gene discoveri studi call mimmin extract textual descript onlin mendelian inherit man omim demonstr dmn contain differ knowledg exist phenotyp data sourc final case studi marfan syndrom prove dmn contain use inform can provid lead discov unknown diseas caus integr dmn system approach mimmin data offer opportun predict novel diseas genet made dmn public avail nlpcaseedupublicdatadmn

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