Comput Biol Chem - A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction.

Tópicos

{ sequenc(1873) structur(1644) protein(1328) }
{ structur(1116) can(940) graph(676) }
{ learn(2355) train(1041) set(1003) }
{ signal(2180) analysi(812) frequenc(800) }
{ sampl(1606) size(1419) use(1276) }
{ high(1669) rate(1365) level(1280) }
{ problem(2511) optim(1539) algorithm(950) }
{ concept(1167) ontolog(924) domain(897) }
{ method(984) reconstruct(947) comput(926) }
{ compound(1573) activ(1297) structur(1058) }
{ model(2656) set(1616) predict(1553) }
{ data(3008) multipl(1320) sourc(1022) }
{ import(1318) role(1303) understand(862) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ analysi(2126) use(1163) compon(1037) }
{ method(1969) cluster(1462) data(1082) }
{ imag(1947) propos(1133) code(1026) }
{ measur(2081) correl(1212) valu(896) }
{ bind(1733) structur(1185) ligand(1036) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ framework(1458) process(801) describ(734) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
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{ featur(1941) imag(1645) propos(1176) }
{ howev(809) still(633) remain(590) }
{ risk(3053) factor(974) diseas(938) }
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{ imag(2675) segment(2577) method(1081) }
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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) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ 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) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }

Resumo

During last few decades accurate determination of protein structural class using a fast and suitable computational method has been a challenging problem in protein science. In this context a meaningful representation of a protein sample plays a key role in achieving higher prediction accuracy. In this paper based on the concept of Chou's pseudo amino acid composition (Chou, K.C., 2001. Proteins 43, 246-255), a new feature representation method is introduced which is composed of the amino acid composition information, the amphiphilic correlation factors and the spectral characteristics of the protein. Thus the sample of a protein is represented by a set of discrete components which incorporate both the sequence order and the length effect. On the basis of such a statistical framework a simple radial basis function network based classifier is introduced to predict protein structural class. A set of exhaustive simulation studies demonstrates high success rate of classification using the self-consistency and jackknife test on the benchmark datasets.

Resumo Limpo

last decad accur determin protein structur class use fast suitabl comput method challeng problem protein scienc context meaning represent protein sampl play key role achiev higher predict accuraci paper base concept chous pseudo amino acid composit chou kc protein new featur represent method introduc compos amino acid composit inform amphiphil correl factor spectral characterist protein thus sampl protein repres set discret compon incorpor sequenc order length effect basi statist framework simpl radial basi function network base classifi introduc predict protein structur class set exhaust simul studi demonstr high success rate classif use selfconsist jackknif test benchmark dataset

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