Comput Biol Chem - A novel k-word relative measure for sequence comparison.

Tópicos

{ measur(2081) correl(1212) valu(896) }
{ error(1145) method(1030) estim(1020) }
{ method(1969) cluster(1462) data(1082) }
{ data(1737) use(1416) pattern(1282) }
{ problem(2511) optim(1539) algorithm(950) }
{ sequenc(1873) structur(1644) protein(1328) }
{ featur(3375) classif(2383) classifi(1994) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ algorithm(1844) comput(1787) effici(935) }
{ perform(999) metric(946) measur(919) }
{ structur(1116) can(940) graph(676) }
{ use(976) code(926) identifi(902) }
{ imag(1947) propos(1133) code(1026) }
{ system(1976) rule(880) can(841) }
{ imag(2830) propos(1344) filter(1198) }
{ assess(1506) score(1403) qualiti(1306) }
{ framework(1458) process(801) describ(734) }
{ method(1557) propos(1049) approach(1037) }
{ control(1307) perform(991) simul(935) }
{ method(984) reconstruct(947) comput(926) }
{ howev(809) still(633) remain(590) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ time(1939) patient(1703) rate(768) }
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{ can(774) often(719) complex(702) }
{ inform(2794) health(2639) internet(1427) }
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{ studi(1410) differ(1259) use(1210) }
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{ compound(1573) activ(1297) structur(1058) }
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{ studi(1119) effect(1106) posit(819) }
{ 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) }
{ 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) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ 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) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
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{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ 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(2212) result(1239) propos(1039) }

Resumo

In order to extract phylogenetic information from DNA sequences, the new normalized k-word average relative distance is proposed in this paper. The proposed measure was tested by discriminate analysis and phylogenetic analysis. The phylogenetic trees based on the Manhattan distance measure are reconstructed with k ranging from 1 to 12. At the same time, a new method is suggested to reduce the matrix dimension, can greatly lessen the amount of calculation and operation time. The experimental assessment demonstrated that our measure was efficient. What's more, comparing with other methods' results shows that our method is feasible and powerful for phylogenetic analysis.

Resumo Limpo

order extract phylogenet inform dna sequenc new normal kword averag relat distanc propos paper propos measur test discrimin analysi phylogenet analysi phylogenet tree base manhattan distanc measur reconstruct k rang time new method suggest reduc matrix dimens can great lessen amount calcul oper time experiment assess demonstr measur effici what compar method result show method feasibl power phylogenet analysi

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