J Biomed Inform - A semi-supervised approach to extract pharmacogenomics-specific drug-gene pairs from biomedical literature for personalized medicine.

Tópicos

{ search(2224) databas(1162) retriev(909) }
{ drug(1928) target(777) effect(648) }
{ extract(1171) text(1153) clinic(932) }
{ method(2212) result(1239) propos(1039) }
{ method(1219) similar(1157) match(930) }
{ problem(2511) optim(1539) algorithm(950) }
{ method(1557) propos(1049) approach(1037) }
{ model(2656) set(1616) predict(1553) }
{ concept(1167) ontolog(924) domain(897) }
{ howev(809) still(633) remain(590) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ state(1844) use(1261) util(961) }
{ gene(2352) biolog(1181) express(1162) }
{ system(1976) rule(880) can(841) }
{ sequenc(1873) structur(1644) protein(1328) }
{ network(2748) neural(1063) input(814) }
{ monitor(1329) mobil(1314) devic(1160) }
{ patient(1821) servic(1111) care(1106) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ can(774) often(719) complex(702) }
{ measur(2081) correl(1212) valu(896) }
{ patient(2315) diseas(1263) diabet(1191) }
{ motion(1329) object(1292) video(1091) }
{ framework(1458) process(801) describ(734) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ data(1714) softwar(1251) tool(1186) }
{ data(3963) clinic(1234) research(1004) }
{ import(1318) role(1303) understand(862) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ use(1733) differ(960) four(931) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ error(1145) method(1030) estim(1020) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ 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) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ 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) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Personalized medicine is to deliver the right drug to the right patient in the right dose. Pharmacogenomics (PGx) is to identify genetic variants that may affect drug efficacy and toxicity. The availability of a comprehensive and accurate PGx-specific drug-gene relationship knowledge base is important for personalized medicine. However, building a large-scale PGx-specific drug-gene knowledge base is a difficult task. In this study, we developed a bootstrapping, semi-supervised learning approach to iteratively extract and rank drug-gene pairs according to their relevance to drug pharmacogenomics. Starting with a single PGx-specific seed pair and 20 million MEDLINE abstracts, the extraction algorithm achieved a precision of 0.219, recall of 0.368 and F1 of 0.274 after two iterations, a significant improvement over the results of using non-PGx-specific seeds (precision: 0.011, recall: 0.018, and F1: 0.014) or co-occurrence (precision: 0.015, recall: 1.000, and F1: 0.030). After the extraction step, the ranking algorithm further improved the precision from 0.219 to 0.561 for top ranked pairs. By comparing to a dictionary-based approach with PGx-specific gene lexicon as input, we showed that the bootstrapping approach has better performance in terms of both precision and F1 (precision: 0.251 vs. 0.152, recall: 0.396 vs. 0.856 and F1: 0.292 vs. 0.254). By integrative analysis using a large drug adverse event database, we have shown that the extracted drug-gene pairs strongly correlate with drug adverse events. In conclusion, we developed a novel semi-supervised bootstrapping approach for effective PGx-specific drug-gene pair extraction from large number of MEDLINE articles with minimal human input.

Resumo Limpo

person medicin deliv right drug right patient right dose pharmacogenom pgx identifi genet variant may affect drug efficaci toxic avail comprehens accur pgxspecif druggen relationship knowledg base import person medicin howev build largescal pgxspecif druggen knowledg base difficult task studi develop bootstrap semisupervis learn approach iter extract rank druggen pair accord relev drug pharmacogenom start singl pgxspecif seed pair million medlin abstract extract algorithm achiev precis recal f two iter signific improv result use nonpgxspecif seed precis recal f cooccurr precis recal f extract step rank algorithm improv precis top rank pair compar dictionarybas approach pgxspecif gene lexicon input show bootstrap approach better perform term precis f precis vs recal vs f vs integr analysi use larg drug advers event databas shown extract druggen pair strong correl drug advers event conclus develop novel semisupervis bootstrap approach effect pgxspecif druggen pair extract larg number medlin articl minim human input

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