J Chem Inf Model - Automated extraction of information on chemical-P-glycoprotein interactions from the literature.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ system(1050) medic(1026) inform(1018) }
{ visual(1396) interact(850) tool(830) }
{ drug(1928) target(777) effect(648) }
{ can(981) present(881) function(850) }
{ studi(2440) review(1878) systemat(933) }
{ search(2224) databas(1162) retriev(909) }
{ bind(1733) structur(1185) ligand(1036) }
{ case(1353) use(1143) diagnosi(1136) }
{ compound(1573) activ(1297) structur(1058) }
{ data(1737) use(1416) pattern(1282) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ framework(1458) process(801) describ(734) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ research(1085) discuss(1038) issu(1018) }
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{ 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) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ 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) }
{ health(1844) social(1437) communiti(874) }
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{ cancer(2502) breast(956) screen(824) }
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{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
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{ decis(3086) make(1611) patient(1517) }
{ method(1969) cluster(1462) data(1082) }

Resumo

Knowledge of the interactions between drugs and transporters is important for drug discovery and development as well as for the evaluation of their clinical safety. We recently developed a text-mining system for the automatic extraction of information on chemical-CYP3A4 interactions from the literature. This system is based on natural language processing and can extract chemical names and their interaction patterns according to sentence context. The present study aimed to extend this system to the extraction of information regarding chemical-transporter interactions. For this purpose, the key verb list designed for cytochrome P450 enzymes was replaced with that for known drug transporters. The performance of the system was then tested by examining the accuracy of information on chemical-P-glycoprotein (P-gp) interactions extracted from randomly selected PubMed abstracts. The system achieved 89.8% recall and 84.2% precision for the identification of chemical names and 71.7% recall and 78.6% precision for the extraction of chemical-P-gp interactions.

Resumo Limpo

knowledg interact drug transport import drug discoveri develop well evalu clinic safeti recent develop textmin system automat extract inform chemicalcypa interact literatur system base natur languag process can extract chemic name interact pattern accord sentenc context present studi aim extend system extract inform regard chemicaltransport interact purpos key verb list design cytochrom p enzym replac known drug transport perform system test examin accuraci inform chemicalpglycoprotein pgp interact extract random select pubm abstract system achiev recal precis identif chemic name recal precis extract chemicalpgp interact

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