J Am Med Inform Assoc - Utility of gene-specific algorithms for predicting pathogenicity of uncertain gene variants.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ model(3404) distribut(989) bayesian(671) }
{ howev(809) still(633) remain(590) }
{ model(2341) predict(2261) use(1141) }
{ extract(1171) text(1153) clinic(932) }
{ bind(1733) structur(1185) ligand(1036) }
{ treatment(1704) effect(941) patient(846) }
{ clinic(1479) use(1117) guidelin(835) }
{ method(2212) result(1239) propos(1039) }
{ algorithm(1844) comput(1787) effici(935) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
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{ method(1557) propos(1049) approach(1037) }
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{ health(3367) inform(1360) care(1135) }
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{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ 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) }
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{ can(981) present(881) function(850) }
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{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ implement(1333) system(1263) develop(1122) }
{ 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) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

The rapid advance of gene sequencing technologies has produced an unprecedented rate of discovery of genome variation in humans. A growing number of authoritative clinical repositories archive gene variants and disease phenotypes, yet there are currently many more gene variants that lack clear annotation or disease association. To date, there has been very limited coverage of gene-specific predictors in the literature. Here the evaluation is presented of "gene-specific" predictor models based on a na?ve Bayesian classifier for 20 gene-disease datasets, containing 3986 variants with clinically characterized patient conditions. The utility of gene-specific prediction is then compared with "all-gene" generalized prediction and also with existing popular predictors. Gene-specific computational prediction models derived from clinically curated gene variant disease datasets often outperform established generalized algorithms for novel and uncertain gene variants.

Resumo Limpo

rapid advanc gene sequenc technolog produc unpreced rate discoveri genom variat human grow number authorit clinic repositori archiv gene variant diseas phenotyp yet current mani gene variant lack clear annot diseas associ date limit coverag genespecif predictor literatur evalu present genespecif predictor model base nave bayesian classifi genediseas dataset contain variant clinic character patient condit util genespecif predict compar allgen general predict also exist popular predictor genespecif comput predict model deriv clinic curat gene variant diseas dataset often outperform establish general algorithm novel uncertain gene variant

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