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Course Data (course + data)

Distribution by Scientific Domains

Mathematics and Statistics	66%

Kinds of Course Data

time course data

Selected Abstracts

On Gene Ranking Using Replicated Microarray Time Course Data

BIOMETRICS, Issue 1 2009
Yu Chuan Tai
Summary Consider the ranking of genes using data from replicated microarray time course experiments, where there are multiple biological conditions, and the genes of interest are those whose temporal profiles differ across conditions. We derive a multisample multivariate empirical Bayes' statistic for ranking genes in the order of differential expression, from both longitudinal and cross-sectional replicated developmental microarray time course data. Our longitudinal multisample model assumes that time course replicates are independent and identically distributed multivariate normal vectors. On the other hand, we construct a cross-sectional model using a normal regression framework with any appropriate basis for the design matrices. In both cases, we use natural conjugate priors in our empirical Bayes' setting which guarantee closed form solutions for the posterior odds. The simulations and two case studies using published worm and mouse microarray time course datasets indicate that the proposed approaches perform satisfactorily. [source]

Estimating winter evaporation in boreal forests with operational snow course data

HYDROLOGICAL PROCESSES, Issue 8 2003
Angela Lundberg
Abstract Snow course measurements from 11 sites located in eastern and northern Finland were used to estimate the total interception evaporation of a winter season for different forest categories. We categorized the sites based on forest density and tree species. Results showed that interception loss from gross precipitation increased with forest density and approached 30% for a forest with the highest density class. Interception loss for the most common forest density class was 11%. Interception losses were slightly larger in spruce forests than in pine, deciduous, or mixed forests. We provide suggestions as to how to design snow surveys to estimate wintertime interception evaporation better. Rough terrain and transition zones between forest and open areas should be avoided. Since evaporation fraction was strongly dependent on tree crown characteristics, snow course data should include direct estimates of canopy closure. Qualitative observations made by different observers should be given a reference frame to ensure comparability of records from different sites. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Bayesian State Space Models for Inferring and Predicting Temporal Gene Expression Profiles

BIOMETRICAL JOURNAL, Issue 6 2007
Yulan Liang
Abstract Prediction of gene dynamic behavior is a challenging and important problem in genomic research while estimating the temporal correlations and non-stationarity are the keys in this process. Unfortunately, most existing techniques used for the inclusion of the temporal correlations treat the time course as evenly distributed time intervals and use stationary models with time-invariant settings. This is an assumption that is often violated in microarray time course data since the time course expression data are at unequal time points, where the difference in sampling times varies from minutes to days. Furthermore, the unevenly spaced short time courses with sudden changes make the prediction of genetic dynamics difficult. In this paper, we develop two types of Bayesian state space models to tackle this challenge for inferring and predicting the gene expression profiles associated with diseases. In the univariate time-varying Bayesian state space models we treat both the stochastic transition matrix and the observation matrix time-variant with linear setting and point out that this can easily be extended to nonlinear setting. In the multivariate Bayesian state space model we include temporal correlation structures in the covariance matrix estimations. In both models, the unevenly spaced short time courses with unseen time points are treated as hidden state variables. Bayesian approaches with various prior and hyper-prior models with MCMC algorithms are used to estimate the model parameters and hidden variables. We apply our models to multiple tissue polygenetic affymetrix data sets. Results show that the predictions of the genomic dynamic behavior can be well captured by the proposed models. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

On Gene Ranking Using Replicated Microarray Time Course Data

Bayesian Inference for Stochastic Kinetic Models Using a Diffusion Approximation

BIOMETRICS, Issue 3 2005
A. Golightly
Summary This article is concerned with the Bayesian estimation of stochastic rate constants in the context of dynamic models of intracellular processes. The underlying discrete stochastic kinetic model is replaced by a diffusion approximation (or stochastic differential equation approach) where a white noise term models stochastic behavior and the model is identified using equispaced time course data. The estimation framework involves the introduction of m, 1 latent data points between every pair of observations. MCMC methods are then used to sample the posterior distribution of the latent process and the model parameters. The methodology is applied to the estimation of parameters in a prokaryotic autoregulatory gene network. [source]