BMC Med Inform Decis Mak - Mining biomarker information in biomedical literature.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ search(2224) databas(1162) retriev(909) }
{ concept(1167) ontolog(924) domain(897) }
{ method(2212) result(1239) propos(1039) }
{ method(1557) propos(1049) approach(1037) }
{ first(2504) two(1366) second(1323) }
{ studi(2440) review(1878) systemat(933) }
{ learn(2355) train(1041) set(1003) }
{ model(2341) predict(2261) use(1141) }
{ perform(1367) use(1326) method(1137) }
{ cost(1906) reduc(1198) effect(832) }
{ research(1085) discuss(1038) issu(1018) }
{ compound(1573) activ(1297) structur(1058) }
{ studi(1119) effect(1106) posit(819) }
{ patient(2315) diseas(1263) diabet(1191) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ import(1318) role(1303) understand(862) }
{ gene(2352) biolog(1181) express(1162) }
{ use(2086) technolog(871) perceiv(783) }
{ use(976) code(926) identifi(902) }
{ survey(1388) particip(1329) question(1065) }
{ process(1125) use(805) approach(778) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ method(1219) similar(1157) match(930) }
{ imag(2830) propos(1344) filter(1198) }
{ assess(1506) score(1403) qualiti(1306) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ visual(1396) interact(850) tool(830) }
{ spatial(1525) area(1432) region(1030) }
{ model(3480) simul(1196) paramet(876) }
{ state(1844) use(1261) util(961) }
{ data(2317) use(1299) case(1017) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(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) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
{ 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) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ 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) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ estim(2440) model(1874) function(577) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

CKGROUND: For selection and evaluation of potential biomarkers, inclusion of already published information is of utmost importance. In spite of significant advancements in text- and data-mining techniques, the vast knowledge space of biomarkers in biomedical text has remained unexplored. Existing named entity recognition approaches are not sufficiently selective for the retrieval of biomarker information from the literature. The purpose of this study was to identify textual features that enhance the effectiveness of biomarker information retrieval for different indication areas and diverse end user perspectives.METHODS: A biomarker terminology was created and further organized into six concept classes. Performance of this terminology was optimized towards balanced selectivity and specificity. The information retrieval performance using the biomarker terminology was evaluated based on various combinations of the terminology's six classes. Further validation of these results was performed on two independent corpora representing two different neurodegenerative diseases.RESULTS: The current state of the biomarker terminology contains 119 entity classes supported by 1890 different synonyms. The result of information retrieval shows improved retrieval rate of informative abstracts, which is achieved by including clinical management terms and evidence of gene/protein alterations (e.g. gene/protein expression status or certain polymorphisms) in combination with disease and gene name recognition. When additional filtering through other classes (e.g. diagnostic or prognostic methods) is applied, the typical high number of unspecific search results is significantly reduced. The evaluation results suggest that this approach enables the automated identification of biomarker information in the literature. A demo version of the search engine SCAIView, including the biomarker retrieval, is made available to the public through http://www.scaiview.com/scaiview-academia.html.CONCLUSIONS: The approach presented in this paper demonstrates that using a dedicated biomarker terminology for automated analysis of the scientific literature maybe helpful as an aid to finding biomarker information in text. Successful extraction of candidate biomarkers information from published resources can be considered as the first step towards developing novel hypotheses. These hypotheses will be valuable for the early decision-making in the drug discovery and development process.

Resumo Limpo

ckground select evalu potenti biomark inclus alreadi publish inform utmost import spite signific advanc text datamin techniqu vast knowledg space biomark biomed text remain unexplor exist name entiti recognit approach suffici select retriev biomark inform literatur purpos studi identifi textual featur enhanc effect biomark inform retriev differ indic area divers end user perspectivesmethod biomark terminolog creat organ six concept class perform terminolog optim toward balanc select specif inform retriev perform use biomark terminolog evalu base various combin terminolog six class valid result perform two independ corpora repres two differ neurodegen diseasesresult current state biomark terminolog contain entiti class support differ synonym result inform retriev show improv retriev rate inform abstract achiev includ clinic manag term evid geneprotein alter eg geneprotein express status certain polymorph combin diseas gene name recognit addit filter class eg diagnost prognost method appli typic high number unspecif search result signific reduc evalu result suggest approach enabl autom identif biomark inform literatur demo version search engin scaiview includ biomark retriev made avail public httpwwwscaiviewcomscaiviewacademiahtmlconclus approach present paper demonstr use dedic biomark terminolog autom analysi scientif literatur mayb help aid find biomark inform text success extract candid biomark inform publish resourc can consid first step toward develop novel hypothes hypothes will valuabl earli decisionmak drug discoveri develop process

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