J Biomed Inform - Finding disease similarity based on implicit semantic similarity.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ method(1219) similar(1157) match(930) }
{ extract(1171) text(1153) clinic(932) }
{ state(1844) use(1261) util(961) }
{ result(1111) use(1088) new(759) }
{ framework(1458) process(801) describ(734) }
{ learn(2355) train(1041) set(1003) }
{ general(901) number(790) one(736) }
{ model(3404) distribut(989) bayesian(671) }
{ concept(1167) ontolog(924) domain(897) }
{ import(1318) role(1303) understand(862) }
{ studi(1119) effect(1106) posit(819) }
{ model(3480) simul(1196) paramet(876) }
{ group(2977) signific(1463) compar(1072) }
{ data(1737) use(1416) pattern(1282) }
{ care(1570) inform(1187) nurs(1089) }
{ howev(809) still(633) remain(590) }
{ cost(1906) reduc(1198) effect(832) }
{ use(976) code(926) identifi(902) }
{ decis(3086) make(1611) patient(1517) }
{ detect(2391) sensit(1101) algorithm(908) }
{ can(774) often(719) complex(702) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ problem(2511) optim(1539) algorithm(950) }
{ chang(1828) time(1643) increas(1301) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ perform(1367) use(1326) method(1137) }
{ record(1888) medic(1808) patient(1693) }
{ research(1218) medic(880) student(794) }
{ medic(1828) order(1363) alert(1069) }
{ sampl(1606) size(1419) use(1276) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
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{ imag(2830) propos(1344) filter(1198) }
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{ 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) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ error(1145) method(1030) estim(1020) }
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{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
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{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ compound(1573) activ(1297) structur(1058) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ 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) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ implement(1333) system(1263) develop(1122) }
{ estim(2440) model(1874) function(577) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

Genomics has contributed to a growing collection of gene-function and gene-disease annotations that can be exploited by informatics to study similarity between diseases. This can yield insight into disease etiology, reveal common pathophysiology and/or suggest treatment that can be appropriated from one disease to another. Estimating disease similarity solely on the basis of shared genes can be misleading as variable combinations of genes may be associated with similar diseases, especially for complex diseases. This deficiency can be potentially overcome by looking for common biological processes rather than only explicit gene matches between diseases. The use of semantic similarity between biological processes to estimate disease similarity could enhance the identification and characterization of disease similarity. We present functions to measure similarity between terms in an ontology, and between entities annotated with terms drawn from the ontology, based on both co-occurrence and information content. The similarity measure is shown to outperform other measures used to detect similarity. A manually curated dataset with known disease similarities was used as a benchmark to compare the estimation of disease similarity based on gene-based and Gene Ontology (GO) process-based comparisons. The detection of disease similarity based on semantic similarity between GO Processes (Recall=55%, Precision=60%) performed better than using exact matches between GO Processes (Recall=29%, Precision=58%) or gene overlap (Recall=88% and Precision=16%). The GO-Process based disease similarity scores on an external test set show statistically significant Pearson correlation (0.73) with numeric scores provided by medical residents. GO-Processes associated with similar diseases were found to be significantly regulated in gene expression microarray datasets of related diseases.

Resumo Limpo

genom contribut grow collect genefunct genediseas annot can exploit informat studi similar diseas can yield insight diseas etiolog reveal common pathophysiolog andor suggest treatment can appropri one diseas anoth estim diseas similar sole basi share gene can mislead variabl combin gene may associ similar diseas especi complex diseas defici can potenti overcom look common biolog process rather explicit gene match diseas use semant similar biolog process estim diseas similar enhanc identif character diseas similar present function measur similar term ontolog entiti annot term drawn ontolog base cooccurr inform content similar measur shown outperform measur use detect similar manual curat dataset known diseas similar use benchmark compar estim diseas similar base genebas gene ontolog go processbas comparison detect diseas similar base semant similar go process recal precis perform better use exact match go process recal precis gene overlap recal precis goprocess base diseas similar score extern test set show statist signific pearson correl numer score provid medic resid goprocess associ similar diseas found signific regul gene express microarray dataset relat diseas

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