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Fully Bayesian Analysis of RNA-seq Counts for the Detection of Gene Expression Heterosis
负责人:
关键词:
Genetics Molecular Biology Biotechnology 69999 Biological Sciences not elsewhere classified 19999 Mathematical Sciences not elsewhere classified 110309 Infectious Diseases Plant Biology
DOI:
doi:10.6084/m9.figshare.6949499
摘要:
parallelized Markov chain Monte Carlo algorithm ameliorates the computational burden. We use our method to detect gene expression heterosis in a two-hybrid plant
Data from: Disentangling the effects of geographic and ecological isolation on genetic differentiation
负责人:
关键词:
Isolation by ecology;spatial genetics;isolation by distance;Zea mays;Landscape Genetics;Homo Sapiens;isolation by environment;partial Mantel test
DOI:
doi:10.5061/dryad.24kp5
摘要:
del are estimated using a Markov chain Monte Carlo algorithm. We call this method Bayesian Estimation of Differentiation in Alleles by Spatial Structure
Data from: Integrating continuous stocks and flows into state-and-transition simulation models of landscape change
负责人:
关键词:
Markov chain;ST-Sim software;Modeling;systems dynamics;carbon budget;stochastic;land use change;spatial;landscape dynamics;Holocene;landscape ecology
DOI:
doi:10.5061/dryad.6939c
摘要:
forward in time, as a discrete-time stochastic process using a Monte Carlo approach, in response to any number of possible transitions. A current limitation
Fully Bayesian analysis of RNA-seq counts for the detection of gene expression heterosis
负责人:
关键词:
Genetics Molecular Biology Biotechnology 69999 Biological Sciences not elsewhere classified 19999 Mathematical Sciences not elsewhere classified 110309 Infectious Diseases Plant Biology
DOI:
doi:10.6084/m9.figshare.6949499.v1
摘要:
an efficient parallelized Markov chain Monte Carlo algorithm ameliorates the computational burden. We use our method to detect gene expression hete
Data from: Estimating range expansion of wildlife in heterogeneous landscapes: a spatially explicit state-space matrix model coupled with an improved
负责人:
Osada, Yutaka
关键词:
population dynamic
DOI:
doi:10.5061/dryad.pt38f8s
摘要:
numerical integration technique (Markov chain Monte Carlo with particle filters). In particular, we explored the environmental drivers of inhomogeneous range expansion
Data from: Molecular evidence for hybridization in Colias (Lepidoptera: Pieridae): are Colias hybrids really hybrids?
负责人:
关键词:
Colias eriphyle;Tertiary;hybridization;Colias eurytheme;introgression
DOI:
doi:10.5061/dryad.n94sg
摘要:
was performed on 378 specimens collected from northern California and Nevada. Population structure was inferred using a Bayesian/Markov chain Monte Carlo
Data from: A Bayesian approach for detecting the impact of mass-extinction events on molecular phylogenies when rates of lineage diversification ma
负责人:
关键词:
Compound Poisson Process Model;Birth-Death Stochastic-Branching Process;Bayesian inference;Lineage Diversification Rates;phylogeny;28 Mass Extinction;speciation
DOI:
doi:10.5061/dryad.v16ns
摘要:
rs of the model are then estimated in a Bayesian statistical framework using a reversible-jump Markov chain Monte Carlo algorithm. This Bayesian approach enables
Data from: Population dynamics of an Arctiid caterpillar-tachinid parasitoid system using state-space models
负责人:
关键词:
Donor control Host-parasitoid Insect outbreaks Markov chain Monte Carlo state space models trophic interactions population census survey
DOI:
doi:10.5061/dryad.sg45t
摘要:
1.?Population dynamics of insect host–parasitoid systems are important in many natural and managed ecosystems and have inspired much
Data from: Full Bayesian comparative phylogeography from genomic data
负责人:
关键词:
Bayesian model choice;Neogene;Gekko crombota;Gekko mindorensis;Biogeography;Quaternary;Gekko rossi;Dirichlet-process prior;phylogeography
DOI:
doi:10.5061/dryad.4b3j2bj
摘要:
del-averaged posterior via Markov chain Monte Carlo algorithms. Using simulations, we find that the new method is much more accurate and precise at estima
Data from: Understanding past population dynamics: Bayesian coalescent-based modeling with covariates
负责人:
关键词:
population genetics;Gaussian Markov Random Fields;phylogenetics;coalescent;Bayesian inference;Phylodynamics;Evolutionary Biology;effective population size
DOI:
doi:10.5061/dryad.mj0hn
摘要:
the posterior distribution, we adapt efficient Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. Incorporating covariates int

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