Efficient sampling for bayesian inference of conjunctive bayesian networks

Bioinformatics

Volumn 28, Issue 18, 2012, Pages 2318-2324

  Sakoparnig, Thomas ^a,b   Beerenwinkel, Niko ^a,b  

^a ETH ZURICH   (Switzerland)

^b SWISS INSTITUTE OF BIOINFORMATICS   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; BAYES THEOREM; GENETICS; GENOMICS; GENOTYPE; HUMAN; MUTATION; NEOPLASM; STATISTICAL MODEL;

ALGORITHMS; BAYES THEOREM; GENOMICS; GENOTYPE; HUMANS; LIKELIHOOD FUNCTIONS; MODELS, STATISTICAL; MUTATION; NEOPLASMS;

EID: 84866451398     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts433     Document Type: Article

References (27)

1. Beerenwinkel,N. and Sullivant,S. (2009) Markov models for accumulating mutations. Biometrika, 96, 1-7.
2. Beerenwinkel,N. et al. (2004) Learning multiple evolutionary pathways from cross-sectional data. J. Comput. Biol., 12, 584-598.
3. Beerenwinkel,N. et al. (2007) Conjunctive Bayesian networks. Bernoulli, 13, 893-909.
4. Beerenwinkel,N. et al. (2011) Learning monotonic genotype-phenotype maps. Stat. Appl. Genet. Molec. Biol., 10, Article 3.
5. Bogojeska,J. et al. (2008) Stability analysis of mixtures of mutagenetic trees. BMC Bioinformatics, 9, 165.
6. Desper,R. et al. (1999) Inferring tree models for oncogenesis from comparative genome hybridization data. J. Comput. Biol., 6, 37-51.
7. Desper,R. et al. (2000) Distance-based reconstruction of tree models for oncogenesis. J. Comput. Biol., 7, 789-803.
8. Fearon,E. and Vogelstein,B. (1990) A genetic model for colorectal tumorigenesis. Cell, 61, 759-767.
9. Gelman,A. (2004) Bayesian Data Analysis, 2nd edn. Chapman & Hall/CRC, Boca Raton, Florida.
10. Gerstung,M. et al. (2009) Quantifying cancer progression with conjunctive Bayesian networks. Bioinformatics, 25, 2809-2815.
11. Gerstung,M. et al. (2011) The temporal order of genetic and pathway alterations in tumorigenesis. PLoS One, 6, e27136.
12. Giudici,P. and Castelo,R. (2003) Improving Markov chain Monte Carlo model search for data mining. Mach. Learn., 50, 127-158.
13. Grzegorczyk,M. and Husmeier,D. (2008) Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move. Mach. Learn., 71, 265-305.
14. Hanahan,D. and Weinberg,R.A. (2011) Hallmarks of cancer: the next generation. Cell, 144, 646-674.
15. Hjelm,M. et al. (2006) New probabilistic network models and algorithms for oncogenesis. J. Comput. Biol., 13, 853-865.
16. Jiang,F. et al. (2000) Construction of evolutionary tree models for renal cell carcinoma from comparative genomic hybridization data. Cancer Res., 60, 6503-6509.
17. Jones,S. et al. (2008) Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science, 321, 1801-1806.
18. Madigan,D. and York,J. (1995) Bayesian graphical models for discrete data. Int. Stat. Rev., 63, 215-232.
19. Parsons,D.W. et al. (2008) An integrated genomic analysis of human glioblastoma multiforme. Science, 321, 1807-1812.
20. Plummer,M. et al. (2006) Coda: convergence diagnosis and output analysis for mcmc. R News, 6, 7-11.
21. Radmacher,M.D. et al. (2001) Graph models of oncogenesis with an application to melanoma. J. Theor. Biol., 212, 535-548.
22. Rahnenführer,J. et al. (2005) Estimating cancer survival and clinical outcome based on genetic tumor progression scores. Bioinformatics, 21, 2438-2446.
23. Szabo,A. and Boucher,K. (2002) Estimating an oncogenetic tree when false negatives and positives are present. Math. Biosci., 176, 219-236.
24. The Cancer Genome Atlas Research Network (2011) Integrated genomic analyses of ovarian carcinoma. Nature, 474, 609-615.
25. Tierney,L. (1994) Markov chains for exploring posterior distributions. Ann. Stat., 22, 1701-1728.
26. von Heydebreck,A. et al. (2004)Maximum likelihood estimation of oncogenetic tree models. Biostatistics, 5, 545-556.
27. Yin,J. et al. (2006) Model selection for mixtures of mutagenetic trees. Stat. Appl. Genet. Molec. Biol., 5, Article17.