J Biomed Inform - Leveraging health social networking communities in translational research.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ health(1844) social(1437) communiti(874) }
{ drug(1928) target(777) effect(648) }
{ research(1085) discuss(1038) issu(1018) }
{ concept(1167) ontolog(924) domain(897) }
{ monitor(1329) mobil(1314) devic(1160) }
{ use(976) code(926) identifi(902) }
{ method(1557) propos(1049) approach(1037) }
{ studi(1410) differ(1259) use(1210) }
{ structur(1116) can(940) graph(676) }
{ use(1733) differ(960) four(931) }
{ process(1125) use(805) approach(778) }
{ method(2212) result(1239) propos(1039) }
{ imag(1947) propos(1133) code(1026) }
{ system(1976) rule(880) can(841) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ algorithm(1844) comput(1787) effici(935) }
{ data(1714) softwar(1251) tool(1186) }
{ model(2220) cell(1177) simul(1124) }
{ data(3963) clinic(1234) research(1004) }
{ system(1050) medic(1026) inform(1018) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ spatial(1525) area(1432) region(1030) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ cost(1906) reduc(1198) effect(832) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ implement(1333) system(1263) develop(1122) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ clinic(1479) use(1117) guidelin(835) }
{ extract(1171) text(1153) clinic(932) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ 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) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Health social networking communities are emerging resources for translational research. We have designed and implemented a framework called HyGen, which combines Semantic Web technologies, graph algorithms and user profiling to discover and prioritize novel associations across disciplines. This manuscript focuses on the key strategies developed to overcome the challenges in handling patient-generated content in Health social networking communities. Heuristic and quantitative evaluations were carried out in colorectal cancer. The results demonstrate the potential of our approach to bridge silos and to identify hidden links among clinical observations, drugs, genes and diseases. In Amyotrophic Lateral Sclerosis case studies, HyGen has identified 15 of the 20 published disease genes. Additionally, HyGen has highlighted new candidates for future investigations, as well as a scientifically meaningful connection between riluzole and alcohol abuse.

Resumo Limpo

health social network communiti emerg resourc translat research design implement framework call hygen combin semant web technolog graph algorithm user profil discov priorit novel associ across disciplin manuscript focus key strategi develop overcom challeng handl patientgener content health social network communiti heurist quantit evalu carri colorect cancer result demonstr potenti approach bridg silo identifi hidden link among clinic observ drug gene diseas amyotroph later sclerosi case studi hygen identifi publish diseas gene addit hygen highlight new candid futur investig well scientif meaning connect riluzol alcohol abus

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