J. Comput. Biol. - An optimization-based sampling scheme for phylogenetic trees.

Tópicos

{ estim(2440) model(1874) function(577) }
{ model(3404) distribut(989) bayesian(671) }
{ howev(809) still(633) remain(590) }
{ structur(1116) can(940) graph(676) }
{ can(774) often(719) complex(702) }
{ framework(1458) process(801) describ(734) }
{ perform(1367) use(1326) method(1137) }
{ motion(1329) object(1292) video(1091) }
{ method(1557) propos(1049) approach(1037) }
{ cost(1906) reduc(1198) effect(832) }
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{ take(945) account(800) differ(722) }
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{ method(1969) cluster(1462) data(1082) }
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{ algorithm(1844) comput(1787) effici(935) }
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{ health(3367) inform(1360) care(1135) }
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{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Much modern work in phylogenetics depends on statistical sampling approaches to phylogeny construction to estimate probability distributions of possible trees for any given input data set. Our theoretical understanding of sampling approaches to phylogenetics remains far less developed than that for optimization approaches, however, particularly with regard to the number of sampling steps needed to produce accurate samples of tree partition functions. Despite the many advantages in principle of being able to sample trees from sophisticated probabilistic models, we have little theoretical basis for concluding that the prevailing sampling approaches do in fact yield accurate samples from those models within realistic numbers of steps. We propose a novel approach to phylogenetic sampling intended to be both efficient in practice and more amenable to theoretical analysis than the prevailing methods. The method depends on replacing the standard tree rearrangement moves with an alternative Markov model in which one solves a theoretically hard but practically tractable optimization problem on each step of sampling. The resulting method can be applied to a broad range of standard probability models, yielding practical algorithms for efficient sampling and rigorous proofs of accurate sampling for heated versions of some important special cases. We demonstrate the efficiency and versatility of the method by an analysis of uncertainty in tree inference over varying input sizes. In addition to providing a new practical method for phylogenetic sampling, the technique is likely to prove applicable to many similar problems involving sampling over combinatorial objects weighted by a likelihood model.

Resumo Limpo

much modern work phylogenet depend statist sampl approach phylogeni construct estim probabl distribut possibl tree given input data set theoret understand sampl approach phylogenet remain far less develop optim approach howev particular regard number sampl step need produc accur sampl tree partit function despit mani advantag principl abl sampl tree sophist probabilist model littl theoret basi conclud prevail sampl approach fact yield accur sampl model within realist number step propos novel approach phylogenet sampl intend effici practic amen theoret analysi prevail method method depend replac standard tree rearrang move altern markov model one solv theoret hard practic tractabl optim problem step sampl result method can appli broad rang standard probabl model yield practic algorithm effici sampl rigor proof accur sampl heat version import special case demonstr effici versatil method analysi uncertainti tree infer vari input size addit provid new practic method phylogenet sampl techniqu like prove applic mani similar problem involv sampl combinatori object weight likelihood model

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