BMC Med Inform Decis Mak - A repository based on a dynamically extensible data model supporting multidisciplinary research in neuroscience.

Tópicos

{ data(3963) clinic(1234) research(1004) }
{ framework(1458) process(801) describ(734) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ visual(1396) interact(850) tool(830) }
{ take(945) account(800) differ(722) }
{ network(2748) neural(1063) input(814) }
{ compound(1573) activ(1297) structur(1058) }
{ spatial(1525) area(1432) region(1030) }
{ first(2504) two(1366) second(1323) }
{ research(1085) discuss(1038) issu(1018) }
{ studi(1410) differ(1259) use(1210) }
{ model(3480) simul(1196) paramet(876) }
{ inform(2794) health(2639) internet(1427) }
{ control(1307) perform(991) simul(935) }
{ method(1969) cluster(1462) data(1082) }
{ motion(1329) object(1292) video(1091) }
{ medic(1828) order(1363) alert(1069) }
{ implement(1333) system(1263) develop(1122) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ patient(2315) diseas(1263) diabet(1191) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ learn(2355) train(1041) set(1003) }
{ method(1557) propos(1049) approach(1037) }
{ case(1353) use(1143) diagnosi(1136) }
{ system(1050) medic(1026) inform(1018) }
{ use(976) code(926) identifi(902) }
{ result(1111) use(1088) new(759) }
{ imag(1057) registr(996) error(939) }
{ imag(2830) propos(1344) filter(1198) }
{ treatment(1704) effect(941) patient(846) }
{ model(2656) set(1616) predict(1553) }
{ drug(1928) target(777) effect(648) }
{ imag(2675) segment(2577) method(1081) }
{ problem(2511) optim(1539) algorithm(950) }
{ chang(1828) time(1643) increas(1301) }
{ concept(1167) ontolog(924) domain(897) }
{ extract(1171) text(1153) clinic(932) }
{ search(2224) databas(1162) retriev(909) }
{ studi(1119) effect(1106) posit(819) }
{ data(2317) use(1299) case(1017) }
{ sampl(1606) size(1419) use(1276) }
{ activ(1138) subject(705) human(624) }
{ can(981) present(881) function(850) }
{ data(1737) use(1416) pattern(1282) }
{ bind(1733) structur(1185) ligand(1036) }
{ featur(3375) classif(2383) classifi(1994) }
{ studi(2440) review(1878) systemat(933) }
{ assess(1506) score(1403) qualiti(1306) }
{ error(1145) method(1030) estim(1020) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ 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) }
{ perform(1367) use(1326) method(1137) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ 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) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ survey(1388) particip(1329) question(1065) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }

Resumo

CKGROUND: Robust, extensible and distributed databases integrating clinical, imaging and molecular data represent a substantial challenge for modern neuroscience. It is even more difficult to provide extensible software environments able to effectively target the rapidly changing data requirements and structures of research experiments. There is an increasing request from the neuroscience community for software tools addressing technical challenges about: (i) supporting researchers in the medical field to carry out data analysis using integrated bioinformatics services and tools; (ii) handling multimodal/multiscale data and metadata, enabling the injection of several different data types according to structured schemas; (iii) providing high extensibility, in order to address different requirements deriving from a large variety of applications simply through a user runtime configuration.METHODS: A dynamically extensible data structure supporting collaborative multidisciplinary research projects in neuroscience has been defined and implemented. We have considered extensibility issues from two different points of view. First, the improvement of data flexibility has been taken into account. This has been done through the development of a methodology for the dynamic creation and use of data types and related metadata, based on the definition of "meta" data model. This way, users are not constrainted to a set of predefined data and the model can be easily extensible and applicable to different contexts. Second, users have been enabled to easily customize and extend the experimental procedures in order to track each step of acquisition or analysis. This has been achieved through a process-event data structure, a multipurpose taxonomic schema composed by two generic main objects: events and processes. Then, a repository has been built based on such data model and structure, and deployed on distributed resources thanks to a Grid-based approach. Finally, data integration aspects have been addressed by providing the repository application with an efficient dynamic interface designed to enable the user to both easily query the data depending on defined datatypes and view all the data of every patient in an integrated and simple way.RESULTS: The results of our work have been twofold. First, a dynamically extensible data model has been implemented and tested based on a "meta" data-model enabling users to define their own data types independently from the application context. This data model has allowed users to dynamically include additional data types without the need of rebuilding the underlying database. Then a complex process-event data structure has been built, based on this data model, describing patient-centered diagnostic processes and merging information from data and metadata. Second, a repository implementing such a data structure has been deployed on a distributed Data Grid in order to provide scalability both in terms of data input and data storage and to exploit distributed data and computational approaches in order to share resources more efficiently. Moreover, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.CONCLUSIONS: Based on such repository, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.

Resumo Limpo

ckground robust extens distribut databas integr clinic imag molecular data repres substanti challeng modern neurosci even difficult provid extens softwar environ abl effect target rapid chang data requir structur research experi increas request neurosci communiti softwar tool address technic challeng support research medic field carri data analysi use integr bioinformat servic tool ii handl multimodalmultiscal data metadata enabl inject sever differ data type accord structur schema iii provid high extens order address differ requir deriv larg varieti applic simpli user runtim configurationmethod dynam extens data structur support collabor multidisciplinari research project neurosci defin implement consid extens issu two differ point view first improv data flexibl taken account done develop methodolog dynam creation use data type relat metadata base definit meta data model way user constraint set predefin data model can easili extens applic differ context second user enabl easili custom extend experiment procedur order track step acquisit analysi achiev processev data structur multipurpos taxonom schema compos two generic main object event process repositori built base data model structur deploy distribut resourc thank gridbas approach final data integr aspect address provid repositori applic effici dynam interfac design enabl user easili queri data depend defin datatyp view data everi patient integr simpl wayresult result work twofold first dynam extens data model implement test base meta datamodel enabl user defin data type independ applic context data model allow user dynam includ addit data type without need rebuild under databas complex processev data structur built base data model describ patientcent diagnost process merg inform data metadata second repositori implement data structur deploy distribut data grid order provid scalabl term data input data storag exploit distribut data comput approach order share resourc effici moreov data manag made possibl friend web interfac drive principl forc preconfigur data type satisfi user dynam configur data model given experi data acquisit program thus make potenti suitabl custom applicationsconclus base repositori data manag made possibl friend web interfac drive principl forc preconfigur data type satisfi user dynam configur data model given experi data acquisit program thus make potenti suitabl custom applic

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