Comput Biol Chem - Computational intelligence techniques in bioinformatics.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ method(1557) propos(1049) approach(1037) }
{ research(1085) discuss(1038) issu(1018) }
{ learn(2355) train(1041) set(1003) }
{ can(981) present(881) function(850) }
{ sequenc(1873) structur(1644) protein(1328) }
{ featur(3375) classif(2383) classifi(1994) }
{ control(1307) perform(991) simul(935) }
{ data(3008) multipl(1320) sourc(1022) }
{ framework(1458) process(801) describ(734) }
{ concept(1167) ontolog(924) domain(897) }
{ network(2748) neural(1063) input(814) }
{ system(1976) rule(880) can(841) }
{ imag(2830) propos(1344) filter(1198) }
{ problem(2511) optim(1539) algorithm(950) }
{ search(2224) databas(1162) retriev(909) }
{ system(1050) medic(1026) inform(1018) }
{ data(2317) use(1299) case(1017) }
{ sampl(1606) size(1419) use(1276) }
{ use(2086) technolog(871) perceiv(783) }
{ implement(1333) system(1263) develop(1122) }
{ method(1969) cluster(1462) data(1082) }
{ data(1737) use(1416) pattern(1282) }
{ bind(1733) structur(1185) ligand(1036) }
{ method(1219) similar(1157) match(930) }
{ assess(1506) score(1403) qualiti(1306) }
{ chang(1828) time(1643) increas(1301) }
{ data(1714) softwar(1251) tool(1186) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ data(3963) clinic(1234) research(1004) }
{ import(1318) role(1303) understand(862) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ group(2977) signific(1463) compar(1072) }
{ activ(1138) subject(705) human(624) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ use(1733) differ(960) four(931) }
{ activ(1452) weight(1219) physic(1104) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ inform(2794) health(2639) internet(1427) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ error(1145) method(1030) estim(1020) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ design(1359) user(1324) use(1319) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ 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) }
{ patient(2837) hospit(1953) medic(668) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ 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) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Computational intelligence (CI) is a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. It is a methodology involving adaptive mechanisms and/or an ability to learn that facilitate intelligent behavior in complex and changing environments, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association and abstraction. The objective of this article is to present to the CI and bioinformatics research communities some of the state-of-the-art in CI applications to bioinformatics and motivate research in new trend-setting directions. In this article, we present an overview of the CI techniques in bioinformatics. We will show how CI techniques including neural networks, restricted Boltzmann machine, deep belief network, fuzzy logic, rough sets, evolutionary algorithms (EA), genetic algorithms (GA), swarm intelligence, artificial immune systems and support vector machines, could be successfully employed to tackle various problems such as gene expression clustering and classification, protein sequence classification, gene selection, DNA fragment assembly, multiple sequence alignment, and protein function prediction and its structure. We discuss some representative methods to provide inspiring examples to illustrate how CI can be utilized to address these problems and how bioinformatics data can be characterized by CI. Challenges to be addressed and future directions of research are also presented and an extensive bibliography is included.

Resumo Limpo

comput intellig ci wellestablish paradigm current system mani characterist biolog comput capabl perform varieti task difficult use convent techniqu methodolog involv adapt mechan andor abil learn facilit intellig behavior complex chang environ system perceiv possess one attribut reason general discoveri associ abstract object articl present ci bioinformat research communiti stateoftheart ci applic bioinformat motiv research new trendset direct articl present overview ci techniqu bioinformat will show ci techniqu includ neural network restrict boltzmann machin deep belief network fuzzi logic rough set evolutionari algorithm ea genet algorithm ga swarm intellig artifici immun system support vector machin success employ tackl various problem gene express cluster classif protein sequenc classif gene select dna fragment assembl multipl sequenc align protein function predict structur discuss repres method provid inspir exampl illustr ci can util address problem bioinformat data can character ci challeng address futur direct research also present extens bibliographi includ

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