J Integr Bioinform - Community structure of non-coding RNA interaction network.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ import(1318) role(1303) understand(862) }
{ data(1737) use(1416) pattern(1282) }
{ first(2504) two(1366) second(1323) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ sequenc(1873) structur(1644) protein(1328) }
{ network(2748) neural(1063) input(814) }
{ general(901) number(790) one(736) }
{ studi(1410) differ(1259) use(1210) }
{ health(3367) inform(1360) care(1135) }
{ age(1611) year(1155) adult(843) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ system(1976) rule(880) can(841) }
{ bind(1733) structur(1185) ligand(1036) }
{ featur(3375) classif(2383) classifi(1994) }
{ take(945) account(800) differ(722) }
{ assess(1506) score(1403) qualiti(1306) }
{ framework(1458) process(801) describ(734) }
{ concept(1167) ontolog(924) domain(897) }
{ method(1557) propos(1049) approach(1037) }
{ model(2220) cell(1177) simul(1124) }
{ compound(1573) activ(1297) structur(1058) }
{ monitor(1329) mobil(1314) devic(1160) }
{ sampl(1606) size(1419) use(1276) }
{ analysi(2126) use(1163) compon(1037) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ implement(1333) system(1263) develop(1122) }
{ process(1125) use(805) approach(778) }
{ detect(2391) sensit(1101) algorithm(908) }
{ 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) }
{ method(1219) similar(1157) match(930) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ care(1570) inform(1187) nurs(1089) }
{ 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) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ 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) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
{ 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) }
{ 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) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

Rapid technological advances have shown that the ratio of non-protein coding genes rises to 98.5% in humans, suggesting that current knowledge on genetic information processing might be largely incomplete. It implies that protein-coding sequences only represent a small fraction of cellular transcriptional information. Here, we examine the community structure of the network defined by functional interactions between non-coding RNAs (ncRNAs) and proteins related bio-macromolecules (PRMs) using a two-fold approach: modularity in bipartite network and k-clique community detection. First, the high modularity scores as well as the distribution of community sizes showing a scaling-law revealed manifestly non-random features. Second, the k-clique sub-graphs and overlaps show that the identified communities of the ncRNA molecules of H. sapiens can potentially be associated with certain functions. These findings highlight the complex modular structure of ncRNA interactions and its possible regulatory roles in the cell.

Resumo Limpo

rapid technolog advanc shown ratio nonprotein code gene rise human suggest current knowledg genet inform process might larg incomplet impli proteincod sequenc repres small fraction cellular transcript inform examin communiti structur network defin function interact noncod rnas ncrnas protein relat biomacromolecul prms use twofold approach modular bipartit network kcliqu communiti detect first high modular score well distribut communiti size show scalinglaw reveal manifest nonrandom featur second kcliqu subgraph overlap show identifi communiti ncrna molecul h sapien can potenti associ certain function find highlight complex modular structur ncrna interact possibl regulatori role cell

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