J. Comput. Biol. - On the inference of dirichlet mixture priors for protein sequence comparison.

Tópicos

{ model(3404) distribut(989) bayesian(671) }
{ framework(1458) process(801) describ(734) }
{ sequenc(1873) structur(1644) protein(1328) }
{ case(1353) use(1143) diagnosi(1136) }
{ structur(1116) can(940) graph(676) }
{ result(1111) use(1088) new(759) }
{ data(3008) multipl(1320) sourc(1022) }
{ concept(1167) ontolog(924) domain(897) }
{ first(2504) two(1366) second(1323) }
{ survey(1388) particip(1329) question(1065) }
{ method(1219) similar(1157) match(930) }
{ network(2748) neural(1063) input(814) }
{ motion(1329) object(1292) video(1091) }
{ error(1145) method(1030) estim(1020) }
{ visual(1396) interact(850) tool(830) }
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{ inform(2794) health(2639) internet(1427) }
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{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ 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) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
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{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ decis(3086) make(1611) patient(1517) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }

Resumo

Dirichlet mixtures provide an elegant formalism for constructing and evaluating protein multiple sequence alignments. Their use requires the inference of Dirichlet mixture priors from curated sets of accurately aligned sequences. This article addresses two questions relevant to such inference: of how many components should a Dirichlet mixture consist, and how may a maximum-likelihood mixture be derived from a given data set. To apply the Minimum Description Length principle to the first question, we extend an analytic formula for the complexity of a Dirichlet model to Dirichlet mixtures by informal argument. We apply a Gibbs-sampling based approach to the second question. Using artificial data generated by a Dirichlet mixture, we demonstrate that our methods are able to approximate well the true theory, when it exists. We apply our methods as well to real data, and infer Dirichlet mixtures that describe the data better than does a mixture derived using previous approaches.

Resumo Limpo

dirichlet mixtur provid eleg formal construct evalu protein multipl sequenc align use requir infer dirichlet mixtur prior curat set accur align sequenc articl address two question relev infer mani compon dirichlet mixtur consist may maximumlikelihood mixtur deriv given data set appli minimum descript length principl first question extend analyt formula complex dirichlet model dirichlet mixtur inform argument appli gibbssampl base approach second question use artifici data generat dirichlet mixtur demonstr method abl approxim well true theori exist appli method well real data infer dirichlet mixtur describ data better mixtur deriv use previous approach

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