Computational discovery of gene regulatory binding motifs: a Bayesian perspective

DOI10.1214/088342304000000107zbMath1057.62101OpenAlexW1994755936MaRDI QIDQ1766324

Shane T. Jensen, X. Shirley Liu, Jun S. Liu, Qing Zhou

Publication date: 7 March 2005

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/088342304000000107

zbMATH Keywords

optimizationMarkov chain Monte CarloBayesian modelsscoring functionsgene regulationmotif discovery

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Biochemistry, molecular biology (92C40) Numerical analysis or methods applied to Markov chains (65C40) Genetics and epigenetics (92D10)

Related Items (8)

Bayesian clustering of DNA sequences using Markov chains and a stochastic partition model ⋮ The EM algorithm and the rise of computational biology ⋮ Convergence rate of Markov chain methods for genomic motif discovery ⋮ Equi-energy sampler with applications in statistical inference and statistical mechanics ⋮ Parallel tempering with equi-energy moves ⋮ Geometric integrators and the Hamiltonian Monte Carlo method ⋮ On the use of statistics in genomics and bioinformatics ⋮ Computational Biology: Toward Deciphering Gene Regulatory Information in Mammalian Genomes

Uses Software

• BioProspector
• BayesDA
• ParaMEME

Cites Work

• Unnamed Item
• Optimization by Simulated Annealing
• Bayesian Models for Multiple Local Sequence Alignment and Gibbs Sampling Strategies
• The Calculation of Posterior Distributions by Data Augmentation
• The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem
• Discovery of Conserved Sequence Patterns Using a Stochastic Dictionary Model
• Bayes Factors

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