J. Comput. Biol. - Gene expression complex networks: synthesis, identification, and analysis.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ network(2748) neural(1063) input(814) }
{ structur(1116) can(940) graph(676) }
{ detect(2391) sensit(1101) algorithm(908) }
{ estim(2440) model(1874) function(577) }
{ method(2212) result(1239) propos(1039) }
{ take(945) account(800) differ(722) }
{ framework(1458) process(801) describ(734) }
{ data(3963) clinic(1234) research(1004) }
{ featur(3375) classif(2383) classifi(1994) }
{ control(1307) perform(991) simul(935) }
{ search(2224) databas(1162) retriev(909) }
{ process(1125) use(805) approach(778) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ perform(999) metric(946) measur(919) }
{ spatial(1525) area(1432) region(1030) }
{ time(1939) patient(1703) rate(768) }
{ drug(1928) target(777) effect(648) }
{ chang(1828) time(1643) increas(1301) }
{ import(1318) role(1303) understand(862) }
{ state(1844) use(1261) util(961) }
{ age(1611) year(1155) adult(843) }
{ group(2977) signific(1463) compar(1072) }
{ use(1733) differ(960) four(931) }
{ can(774) often(719) complex(702) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ algorithm(1844) comput(1787) effici(935) }
{ data(1714) softwar(1251) tool(1186) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ studi(1410) differ(1259) use(1210) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ monitor(1329) mobil(1314) devic(1160) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ sampl(1606) size(1419) use(1276) }
{ use(2086) technolog(871) perceiv(783) }
{ health(1844) social(1437) communiti(874) }
{ cancer(2502) breast(956) screen(824) }
{ activ(1452) weight(1219) physic(1104) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ design(1359) user(1324) use(1319) }
{ model(2220) cell(1177) simul(1124) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ risk(3053) factor(974) diseas(938) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ 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) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ method(1969) cluster(1462) data(1082) }

Resumo

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erd?s-R?nyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barab?si-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree <k> variation, decreasing its network recovery rate with the increase of <k>. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

Resumo Limpo

thank recent advanc molecular biolog alli ever increas amount experiment data function state thousand gene can now extract simultan use method cdna microarray rnaseq particular import relat investig model identif gene regulatori network express data set knowledg fundament mani applic diseas treatment therapeut intervent strategi drug design well plan highthroughput new experi method develop gene network model identif express profil howev import open problem regard valid approach result work present object approach valid gene network model identif compris follow three main aspect artifici gene network agn model generat theoret model complex network use simul tempor express data comput method gene network identif simul data found featur select approach target gene fix express profil observ gene order identifi relev subset predictor valid identifi agnbas network comparison origin network propos framework allow sever type agn generat use order simul tempor express data result network identif method can compar origin network order estim properti accuraci import theoret model complex network assess uniformlyrandom erdsrnyi er smallworld wattsstrogatz ws scalefre barabsialbert ba geograph network gg experiment result indic infer method sensit averag degre k variat decreas network recoveri rate increas k signal size import infer method get better accuraci network identif rate present good result small express profil howev adopt infer method sensibl recogn distinct structur interact among gene present similar behavior appli differ network topolog summari propos framework though simpl adequ valid infer network identifi properti evalu method can extend infer method

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