Comput. Biol. Med. - Improving protein complex classification accuracy using amino acid composition profile.

Tópicos

{ sequenc(1873) structur(1644) protein(1328) }
{ import(1318) role(1303) understand(862) }
{ featur(3375) classif(2383) classifi(1994) }
{ ehr(2073) health(1662) electron(1139) }
{ analysi(2126) use(1163) compon(1037) }
{ compound(1573) activ(1297) structur(1058) }
{ structur(1116) can(940) graph(676) }
{ bind(1733) structur(1185) ligand(1036) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ estim(2440) model(1874) function(577) }
{ studi(2440) review(1878) systemat(933) }
{ studi(1410) differ(1259) use(1210) }
{ system(1050) medic(1026) inform(1018) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ health(3367) inform(1360) care(1135) }
{ data(2317) use(1299) case(1017) }
{ gene(2352) biolog(1181) express(1162) }
{ high(1669) rate(1365) level(1280) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
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{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ 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) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
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{ chang(1828) time(1643) increas(1301) }
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{ care(1570) inform(1187) nurs(1089) }
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{ 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) }
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{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ 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) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ 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) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Protein complex prediction approaches are based on the assumptions that complexes have dense protein-protein interactions and high functional similarity between their subunits. We investigated those assumptions by studying the subunits' interaction topology, sequence similarity and molecular function for human and yeast protein complexes. Inclusion of amino acids' physicochemical properties can provide better understanding of protein complex properties. Principal component analysis is carried out to determine the major features. Adopting amino acid composition profile information with the SVM classifier serves as an effective post-processing step for complexes classification. Improvement is based on primary sequence information only, which is easy to obtain.

Resumo Limpo

protein complex predict approach base assumpt complex dens proteinprotein interact high function similar subunit investig assumpt studi subunit interact topolog sequenc similar molecular function human yeast protein complex inclus amino acid physicochem properti can provid better understand protein complex properti princip compon analysi carri determin major featur adopt amino acid composit profil inform svm classifi serv effect postprocess step complex classif improv base primari sequenc inform easi obtain

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