Statistical approaches for biological networks
Seminar

Literature list

• Bayesian hierarchical models, model selection
  1. Follestad T. et al. (2010). A Bayesian hierarchical model for quantitative Real-time PCR data. Stat. Appl. Genetics and Mol. Biol. 9(1), article 3.
  2. Yang R. et al. (2010). Bayesian model selection for characterizing genomic imprinting effects and patterns. Bioinformatics 26(2), 235-241.
• Bayesian mixture models
  1. Fong Y. et al. (2010). Bayesian mixture modeling using a hybrid sampler with application to protein subfamily identification. Biostat. 11(1), 18-33.
  2. Frühwirt-Schnatter S., Pyne S. (2010). Bayesian inference for finite mixtures of univariate skew-normal and skew-t distributions. Biostat. 11(2), 317-336.
  3. Noma H. et al. (2010). Bayesian ranking and selection methods using hierarchical mixture models in microarray studies. Biostat. 11(2), 281-289.
  4. Kim M. et al. (2010). Mixture-model based estimation of gene expression variance from public database improves identification of differentially expressed genes in small sized microarray data. Bioinformatics 26(4), 486-492.
• Network reconstruction
  1. Charbonnier C. et al. (2010). Weighted-LASSO for structured network inference from time course data. Stat. Appl. Genetics and Mol. Biol. 9(1), article 15.
  2. Anjum et al. (2009). A boosting approach to structure learning of graphs with and without prior knowledge. Bioinformatics 25(22), 2929-2936.
  3. Rau A. et al. (2010). An empirical Bayesian method for estimating biological networks from temporal microarray data. Stat. Appl. in Genetics and Mol. Biol. 9(1), article 9.