J Biomed Inform - A network-theoretic approach for decompositional translation across Open Biological Ontologies.

Tópicos

{ concept(1167) ontolog(924) domain(897) }
{ compound(1573) activ(1297) structur(1058) }
{ structur(1116) can(940) graph(676) }
{ method(1219) similar(1157) match(930) }
{ method(1557) propos(1049) approach(1037) }
{ search(2224) databas(1162) retriev(909) }
{ framework(1458) process(801) describ(734) }
{ measur(2081) correl(1212) valu(896) }
{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ risk(3053) factor(974) diseas(938) }
{ system(1050) medic(1026) inform(1018) }
{ studi(1119) effect(1106) posit(819) }
{ use(976) code(926) identifi(902) }
{ implement(1333) system(1263) develop(1122) }
{ method(1969) cluster(1462) data(1082) }
{ can(774) often(719) complex(702) }
{ imag(2830) propos(1344) filter(1198) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ problem(2511) optim(1539) algorithm(950) }
{ control(1307) perform(991) simul(935) }
{ general(901) number(790) one(736) }
{ featur(1941) imag(1645) propos(1176) }
{ health(3367) inform(1360) care(1135) }
{ gene(2352) biolog(1181) express(1162) }
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{ data(1737) use(1416) pattern(1282) }
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{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ featur(3375) classif(2383) classifi(1994) }
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{ assess(1506) score(1403) qualiti(1306) }
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{ error(1145) method(1030) estim(1020) }
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{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
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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) }
{ 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) }
{ cost(1906) reduc(1198) effect(832) }
{ 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) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ 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) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Biological ontologies are now being widely used for annotation, sharing and retrieval of the biological data. Many of these ontologies are hosted under the umbrella of the Open Biological Ontologies Foundry. In order to support interterminology mapping, composite terms in these ontologies need to be translated into atomic or primitive terms in other, orthogonal ontologies, for example, gluconeogenesis (biological process term) to glucose (chemical ontology term). Identifying such decompositional ontology translations is a challenging problem. In this paper, we propose a network-theoretic approach based on the structure of the integrated OBO relationship graph. We use a network-theoretic measure, called the clustering coefficient, to find relevant atomic terms in the neighborhood of a composite term. By eliminating the existing GO to ChEBI Ontology mappings from OBO, we evaluate whether the proposed approach can re-identify the corresponding relationships. The results indicate that the network structure provides strong cues for decompositional ontology translation and the existing relationships can be used to identify new translations.

Resumo Limpo

biolog ontolog now wide use annot share retriev biolog data mani ontolog host umbrella open biolog ontolog foundri order support interterminolog map composit term ontolog need translat atom primit term orthogon ontolog exampl gluconeogenesi biolog process term glucos chemic ontolog term identifi decomposit ontolog translat challeng problem paper propos networktheoret approach base structur integr obo relationship graph use networktheoret measur call cluster coeffici find relev atom term neighborhood composit term elimin exist go chebi ontolog map obo evalu whether propos approach can reidentifi correspond relationship result indic network structur provid strong cue decomposit ontolog translat exist relationship can use identifi new translat

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