J Chem Inf Model - Classification of compounds with distinct or overlapping multi-target activities and diverse molecular mechanisms using emerging chemical patterns.

Tópicos

{ model(2656) set(1616) predict(1553) }
{ compound(1573) activ(1297) structur(1058) }
{ learn(2355) train(1041) set(1003) }
{ studi(2440) review(1878) systemat(933) }
{ general(901) number(790) one(736) }
{ featur(3375) classif(2383) classifi(1994) }
{ treatment(1704) effect(941) patient(846) }
{ studi(1410) differ(1259) use(1210) }
{ studi(1119) effect(1106) posit(819) }
{ activ(1138) subject(705) human(624) }
{ use(976) code(926) identifi(902) }
{ detect(2391) sensit(1101) algorithm(908) }
{ data(1737) use(1416) pattern(1282) }
{ assess(1506) score(1403) qualiti(1306) }
{ error(1145) method(1030) estim(1020) }
{ design(1359) user(1324) use(1319) }
{ model(2220) cell(1177) simul(1124) }
{ research(1085) discuss(1038) issu(1018) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ perform(1367) use(1326) method(1137) }
{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
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{ can(774) often(719) complex(702) }
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{ inform(2794) health(2639) internet(1427) }
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{ method(1219) similar(1157) match(930) }
{ 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) }
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{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ chang(1828) time(1643) increas(1301) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ control(1307) perform(991) simul(935) }
{ care(1570) inform(1187) nurs(1089) }
{ 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) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ 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) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ 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) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }

Resumo

The emerging chemical patterns (ECP) approach has been introduced for compound classification. Thus far, only very few ECP applications have been reported. Here, we further investigate the ECP methodology by studying complex classification problems. The analysis involves multi-target data sets with systematically organized subsets of compounds having distinct or overlapping target activities and, in addition, data sets containing classes of specifically active compounds with different mechanism-of-action. In systematic classification trials focusing on individual compound subsets or mechanistic classes, ECP calculations utilizing numerical descriptors achieve moderate to high sensitivity, dependent on the data set, and consistently high specificity. Accurate ECP predictions are already obtained on the basis of very small learning sets with only three positive training instances, which distinguishes the ECP approach from many other machine learning techniques.

Resumo Limpo

emerg chemic pattern ecp approach introduc compound classif thus far ecp applic report investig ecp methodolog studi complex classif problem analysi involv multitarget data set systemat organ subset compound distinct overlap target activ addit data set contain class specif activ compound differ mechanismofact systemat classif trial focus individu compound subset mechanist class ecp calcul util numer descriptor achiev moder high sensit depend data set consist high specif accur ecp predict alreadi obtain basi small learn set three posit train instanc distinguish ecp approach mani machin learn techniqu

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