Journal Article10.1016/J.CSDA.2008.07.015
Bayesian models for two-sample time-course microarray experiments
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TL;DR: The proposed procedure successfully deals with various technical difficulties which arise in microarray time-course experiments, such as small number of observations, non-uniform sampling intervals and presence of missing data or repeated measurements, thus offering a good compromise between nonparametric and normality assumption based techniques.
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About: This article is published in Computational Statistics & Data Analysis. The article was published on 01 Mar 2009. The article focuses on the topics: Bayesian probability & Missing data.
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Citations
CMRF: analyzing differential gene regulation in two group perturbation experiments
TL;DR: The problem of finding primarily differentially regulated genes in the presence of external perturbations when the data is sampled from two groups is solved and the probabilistic Bayesian method CMRF based on Markov Random Field incorporates dependency structure of the gene networks as the prior to the model.
Identifying differentially regulated genes
Nirmalya Bandyopadhyay,Manas Somaiya,Sanjay Ranka,Tamer Kahveci +3 more
- 03 Feb 2011
TL;DR: A new probabilistic Bayesian method with Markov Random Field to find genes that respond differently across the two groups in two group perturbation experiments, which is based on information about relationship from gene networks as prior information.
Functional modelling of microarray time series with covariate curves
Maurice Berk,Giovanni Montana +1 more
TL;DR: The model presented here is a specialisation of the general functional mixed-effects model (Rice andWu, 2001; Guo, 2002) and, to the best of the knowledge, it is the first to show how to derive the maximumlikelihood estimators, EM-algorithm, confidence intervals and smoother matrix with more than one fixed-effects function.
Modeling perturbations in gene regulatory and signaling networks
Sanjay Ranka,Tamer Kahveci,Nirmalya Bandyopadhyay +2 more
- 01 Jan 2011
TL;DR: This thesis establishes the justification behind this hypothesis that integrating gene networks with gene expression enables us to analyze the effect of perturbations in a more effective and comprehensive fashion and produces more accurate methods compared to the existing ones.
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Bayesian Models for the Multi-sample Time-Course Microarray Experiments
Claudia Angelini,Daniela De Canditiis,Marianna Pensky,Naomi C. Brownstein +3 more
- 30 Jun 2011
TL;DR: The proposed procedure deals successfully with various technical difficulties which arise in microarray time-course experiments such as a small number of observations, non-uniform sampling intervals and presence of missing data or repeated measurements.
References
Tables of Integrals, Series, and Products
Abstract: Introduction. Elementary Functions. Indefinite Integrals of Elementary Functions. Definite Integrals of Elementary Functions. Indefinite Integrals of Special Functions. Definite Integrals of Special Functions. Special Functions. Vector Field Theory. Algebraic Inequalities. Integral Inequalities. Matrices and Related Results. Determinants. Norms. Ordinary Differential Equations. Fourier, Laplace, and Mellin Transforms. Bibliographic References. Classified Supplementary References.
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Statistical Decision Theory and Bayesian Analysis
James O. Berger
- 22 Dec 2012
TL;DR: An overview of statistical decision theory, which emphasizes the use and application of the philosophical ideas and mathematical structure of decision theory.
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