Bayesian methods in bioinformatics and computational systems biology

Briefings in Bioinformatics

Volumn 8, Issue 2, 2007, Pages 109-116

  Wilkinson, Darren J ^a  

^a NEWCASTLE UNIVERSITY   (United Kingdom)

Author keywords

Bayesian inference; Computational systems biology; Graphical models; Networks; Predictive biology; Quantitative

Indexed keywords

ALGORITHM; BAYES THEOREM; BIOINFORMATICS; COMPUTER MODEL; COMPUTER NETWORK; COMPUTER SYSTEM; DATA ANALYSIS; GENOME ANALYSIS; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MEDICAL RESEARCH; MICROARRAY ANALYSIS; PREDICTION; PROTEIN DETERMINATION; QUANTITATIVE ANALYSIS; REVIEW; SEQUENCE ANALYSIS; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGY; DNA MICROARRAY; GENE EXPRESSION PROFILING; METABOLISM; METHODOLOGY; PHYSIOLOGY; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY;

PROTEOME;

ARTIFICIAL INTELLIGENCE; BAYES THEOREM; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; PROTEOME; SEQUENCE ANALYSIS; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY;

EID: 34250658998     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbm007     Document Type: Review

References (102)

1. Beaumont MA, Rannala B. The Bayesian revolution in genetics. Nat Rev Genet 2004;5:251-61
2. O'Hagan A, Forster JJ. Bayesian Inference. In: Kendall's Advanced Theory of Statistics, Vol. 2B, London: Arnold, 2004
3. Metropolis N, Rosenbluth AW, Rosenbluth MN, et al. Equations of state calculations by fast computing machines. J Chem Phys 1953; 21:1087-92
4. Gamerman D. Markov Chain Monte Carlo. Texts in Statistical Science New York: Chapman and Hall, 1997
5. Brooks SP. Markov chain Monte Carlo method and its application. Statistician 1998;47:69-100
6. Lauritzen SL. Graphical Models. Oxford: Oxford Science Publications, 1996
7. Geman S, Geman D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 1984;6:721-41
8. Cassella G, George EI. Explaining the Gibbs sampler. Am Stat 1992;46:167-74
9. Hastings WK. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 1970;57:97-109
10. Tierney L. Markov chains for exploring posterior distributions (with discussion). Ann Statist 1994;21:1701-62
11. Geyer CJ. Practical Markov chain Monte Carlo. Statistical Sci 1992;7:473-511
12. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis. Boca Raton, FL: Chapman & Hall/CRC Press, 2nd edn, 2003
13. Durbin R, Eddy SR, Krogh A, Mitchison G. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge: Cambridge University Press, 1998
14. Bishop MJ, Thompson EA. Maximum likelihood alignment of DNA sequences. J Mol Biol 1986;190:159-65
15. Churchill GA. Stochastic models for heterogeneous DNA sequences. Bull Math Biol 1989;51:79-94
16. Liu JS, Lawrence CE. Bayesian inference on biopolymer models. Bioinformatics 1999;15:38-52
17. Liu JS, Neuwald AF, Lawrence CE. Bayesian models for multiple local sequence alignment and Gibbs sampling strategies. J Am Stat Assoc 1995;90:1156-70
18. Liu J, Neuwald A, Lawrence C. Markovian structures in biological sequence alignments. J Am Stat Assoc 1999; 94:1-15
19. Zhou Q, Liu JS. Modelling within-motif dependence for transcription factor binding site predictions. Bioinformatics 2004;20 909-16
20. Narlikar L, Gordân R, Ohler U, Hartemink AJ. Informative priors based on transcription factor structural class improve de novo motif discovery. Bioinformatics 2006;22:e384-92, ISMB06
21. Schmidler SC, Liu JS, Brutlag DL. Bayesian segmentation of protein secondary structure. J Comput Biol 2000;7:233-48
22. Lunter G, Miklós I, Drummond A, et al. Bayesian coestimation of phylogeny and sequence alignment. BMC Bioinformatics 2005;6 83
23. Boys RJ, Henderson DA, Wilkinson DJ. Detecting homogeneous segments in DNA sequences by using hidden Markov models. J R Stat Soc 2000; C:49:269-85
24. Green PJ. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 1995;82:711-32
25. Boys RJ, Henderson DA. A Bayesian approach to DNA sequence segmentation (with discussion). Biometrics 2004;60:573-88
26. Liu JS, Logvinenko T. Bayesian methods in biological sequence analysis. In: Balding DJ, Bishop M, Cannings C (eds). Handbook of Statistical Genetics, 2nd edn, Chap. 3, New York: Wiley, 2003
27. Speed TP (ed.). Statistical Analysis of Gene Expression Microarray Data. Florida: Chapman & Hall/CRC, Boca Raton, 2003
28. Wit E, McClure J. Statistics for Microarrays: Design, Analysis and Inference. New York: Wiley, 2004
29. Vanucci M, Do K-A, Müller P (eds). Bayesian Inference for Gene Expression and Proteomics. New York: Cambridge University Press, 2006
30. Zhang D, Wells MT, Smart CD, Fry W. Bayesian normalization an identification for differential gene expression data. J Comput Biol 2005;12:391-406
31. Bhattacharjee M, Pritchard CC, Nelson PS, Arjas E. Bayesian integrated functional analysis of microarray data. Bioinformatics 2004;20 2943-53
32. Frigessi A, van de Wiel MA, Holden M, et al. Genome-wide estimation of transcript concentrations from spotted cDNA microarray data. Nucleic Acids Res, 2005;17:e143
33. van de Wiel MA, Holden M, Glad IK, et al. Bayesian process-based modeling of two-channel microarray experiments: Estimating absolute mRNA concentrations. In: Vanucci M, Do K-A, Müller P, (eds). Bayesian Inference for Gene Expression and Proteomics. Cambridge University Press, New York, 2006, 75-96.
34. Conlon EM, Song JJ, Liu JS. Bayesian models for pooling microarray studies with multiple sources of variation. BMC Bioinformatics 2006;7:247
35. Hein A-MK, Richardson S, Causton HC et al. BGX: A fully Bayesian integrated approach to the analysis of Affymetrix GeneChip data. Biostatistics 2005;6:349-73
36. Lewin A, Richardson S, Marshall C et al. Bayesian modelling of differential gene expression. Biometrics 2006;62:10-8
37. Yeung K, Fraley C, Murua A et al. Model-based clustering and data transformations for gene expression data. Bioinformatics 2001; 17:977-87
38. Wakefield JC, Zhou C, Self SG. Modelling gene expression data over time: curve clustering with informative prior distributions. In: Bernardo J-M, Bayarri MJ, Berger JO et al. (eds). Bayesian Statistics 7. Oxford: Oxford University Press, 2003, 721-32
39. Heard NA, Holmes CC, Stephens DA et al. Bayesian coclustering of Anopheles gene expression time series: Study of immune defense response to multiple experimental challenges. Proceedings of the National Acadamy of Sciences 2005;102:16939-44
40. Gottardo R, Besag J, Stephens M, Murua A. Probabilistic segmentation and intensity estimation for microarray images. Biostatistics 2006; 7:85-99
41. West M, Blanchette C, Dresden H et al. Predicting the clinical status of human breast cancer utilizing gene expression profiles. Proc Natl Acad Sci 2001;98:11462-67
42. Ibrahim JG, Chen M-H, Gray RJ. Bayesian models for gene expression with DNA microarray data. J Am Stat Assoc 2002;97:88-99
43. Gottardo R, Pannucci JA, Kuske CR, Brettin T. Statistical analysis of microarray data: A Bayesian approach. Biostatistics 2003;4 597-620
44. Mertens BJA. On the application of logistic regression modelling in microarray studies. In: Bernardo J-M, Bayarri MJ, Berger JO et al. (eds). Bayesian Statistics 7. Oxford: Oxford University Press, 2003, 607-18
45. West M. Bayesian factor regression models in the 'large p, small n' paradigm. In: Bernardo J-M, Bayarri MJ, Berger JO et al. (eds). Bayesian Statistics 7. Oxford: Oxford University Press, 2003, 733-42
46. Gottardo R, Raftery AE, Yeung KY, Bumgarner RE. Bayesian robust inference for differential gene expression in microarrays with multiple samples. Biometrics 2006;62:10-18
47. Green PJ, Mardia KV. Bayesian alignment using hierarchical models with applications in protein bioinformatics. Biometrika 2006;93 235-54
48. Schmidler SC. Fast Bayesian shape matching using geometric algorithms. In: Bernardo J-M, Bayarri MJ, Berger JO et al. (eds). Bayesian Statistics 8, Oxford: Oxford University Press, 2007, in press
49. Mardia KV, Green PJ, Nyirongo VB et al. Bayesian refinement of protein functional site matching. BMC Bioinformatics 2007, in press
50. Jansen R, Yu H, Greenbaum D et al. Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 2003;302:449-53
51. Morris JS, Brown PJ, Baggerly K, Coombes K. Analysis of mass spectrometry data using Bayesian wavelet-based functional mixed models. In: Vanucci M, Do K-A, Müller P, (eds). Bayesian Inference for Gene Expression and Proteomics, Chap 14. New York: Cambridge University Press, 2006
52. Clyde M, House L, Wolpert R. Nonparametric models for proteomic peak identification and quantification. In: Vanucci M, Do K-A, Müller P (eds). Bayesian Inference for Gene Expression and Proteomics, Chap 15. New York: Cambridge University Press, 2006
53. Bensmail H, Golek J, Moody MM, et al. A novel approach for clustering proteomics data using Bayesian fast Fourier transform. Bioinformatics 2005;21:2210-24
54. Saksena A, Lucarelli D, Wang I-J. Bayesian model selection for mining mass spectrometry data. Neural Netw 2005;18:843-9
55. Zhang W, Chait BT. Profound: An expert system for protein identification using mass spectrometric peptide mapping information. Ann Chem 2000;72:2482-89
56. Chen SS, Deutsch EW, Yi EC, et al. Improving mass and liquid chromatography based identification of proteins using Bayesian scoring. J Proteome Res 2005;4:2174-84
57. Kitano H. Computational systems biology. Nature 2002; 420 206-210
58. Friedman N, Linial M, Nachman I, Pe'er D. Using Bayesian networks to analyse expression data. J Comput Biol 2000;7:601-20
59. Friedman N. Inferring cellular networks using probabilistic graphical models. Science 2004;303:799-805
60. Pournara I, Wernisch L. Reconstruction of gene networks using Bayesian learning and manipulation experiments. Bioinformatics 2004;20 2934-42
61. Schäfer J, Strimmer K. An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 2005;21 754-64
62. Werhli AV, Grzegorczyk M, Husmeier D. Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks. Bioinformatics 2006;22 2523-31
63. Sachs K, Perez O, Pe'er D, et al. Causal protein-signaling networks derived from multiparameter single-cell data. Science 2005;308:523-29
64. Dobra A, Hans C, Jones B et al. Sparse graphical models for exploring gene expression data. J Multivariate Anal 2004;90 196-212
65. Jones B, Carvalho C, Dobra A. et al. Experiments in stochastic computation for high-dimensional graphical models. Statist Sci 2005;20:388-400
66. Giudici P, Green PJ. Decomposable graphical Gaussian model determination. Biometrika 1999;86:785-801
67. Husmeier D. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 2003; 19:2271-82
68. Yu J, Smith VA, Wang PP, et al. Advances to Bayesian network inference for generating causal networks from observational data. Bioinformatics 2004;20:3594-603
69. Opgen-Rhein R, Strimmer K. Learning causal networks from systems biology time course data: An effective model selection procedure for the vector autoregressive process. Bioformatics 2007;8: PMSB06 supplement, in press
70. Lee I, Date SV, Adai AT, Marcotte EM. A probabilistic functional network of yeast genes. Science 2004;306:1555-58
71. Bernard A, Hartemink AJ. Informative structure priors: Joint learning of dynamic regulatory networks from multiple types of data. In: Altman R, Dunker AK, Hunter L, et al. (eds). Pacific Symposium on Biocomputing 2005, New Jersey: World Scientific. 2005, 459-70
72. West M, Ginsburg GS, Huang AT, Nevins JR. Embracing the complexity of genomic data for personalised medicine. Genome Res 2006;16 559-66
73. Nachman I, Regev A, Friedman N. Inferring quantitative models of regulatory networks from expression data. Bioinformatics 2004; 20[supp. 1]:i248-56
74. Ronen M, Rosenberg R, Shraiman BI, Alon U. Assigning numbers to the arrows: Parameterising a gene regulation network by using accurate expression kinetics. Proc Nat Acad Sci 2002;99:10555-60
75. Moles CG, Mendes P, Banga JR. Parameter estimation in biochemical pathways: A comparison of global optimization methods. Genome Res 2003;13:2467-74
76. Gadkar KG, Gunawan R, Doyle FJ III. Iterative approach to model identification of biological networks. BMC Bioinformatics 2005; 6:155
77. Brown KS, Sethna JP. Statistical mechanical approaches to models with many poorly known parameters. Phys Rev E 2003;68:021904
78. Barenco M, Tomescu D, Brewer D, et al. Ranked prediction of p53 targets using hidden variable dynamic modeling. Genome Biol 2006;7:R25
79. Liebermeister W, Klipp E. Biochemical networks with uncertain parameters. IEE Syst Biol 2005;152:97-107
80. Orlando D, Lin C, Bernard A, et al. A probabilistic model for cell cycle distributions in synchrony experiments. Cell Cycle 2007;6(4):478-488
81. Bahcall OG. Single cell resolution in regulation of gene xpression. Mol Syst Biol, 2005;1, doi:10.1038/msb4100020
82. McAdams HH, Arkin A. Stochastic mechanisms in gene expression. Proc Nat Acad Sci USA 1997;94:814-9
83. Arkin A, Ross J, McAdams HH. Stochastic kinetic analysis of developmental pathway bifurcation in phage λ-infected Escherichia coli cells. Genetics 1998;149:1633-48
84. McAdams HH, Arkin A. It's a noisy business: Genetic regulation at the nanomolecular scale. Trends Genet 1999; 15:65-9
85. Wilkinson DJ. Stochastic Modelling for Systems Biology. Boca Raton, Florida: Chapman & Hall/CRC Press, 2006
86. Pepperkok R, Ellenberg J. High-throughput fluorescence microscopy for systems biology. Nat Rev Mol Cell Biol 2006;7:690-6
87. Shen H, Nelson G, Nelson DE, et al. Automated tracking of gene expression profiles in individual cells and cell compartments. J R Soc Interface 2006;3:787-794
88. Reinker S, Altman RM, Timmer J. Parameter estimation in stochastic biochemical reactions. IEE Syst Biol 2006; 153:168-78
89. Boys RJ, Wilkinson DJ, Kirkwood TBL. Bayesian inference for a discretely observed stochastic kinetic model, in press
90. Rempala GA, Ramos KS, Kalbfleisch T. A stochastic model of gene transcription: An application to L1 retrotransposition events. J Theor Biol 2006;242:101-16
91. Gillespie DT. The chemical Langevin equation. J Chem Phys 2000; 113:297-306
92. Golightly A, Wilkinson DJ. Bayesian inference for stochastic kinetic models using a diffusion approximation. Biometrics 2005;61 781-88
93. Doucet A, de Freitas N, Gordon N (eds). Sequential Monte Carlo Methods in Practice. New York: Springer, 2001
94. Golightly A, Wilkinson DJ. Bayesian sequential inference for nonlinear multivariate diffusions. Statist Comput 2006; 16:323-38
95. Golightly A, Wilkinson DJ. Bayesian sequential inference for stochastic kinetic biochemical network models. J Comput Biol 2006;13 838-51
96. Golightly A, Wilkinson DJ. Bayesian inference for nonlinear multivariate diffusion models observed with error, in press
97. Jaqaman K, Danuser G. Linking data to models: Data regression. Nat Rev Mol Cell Biol 2006;7:813-19
98. O'Hagan A. Bayesian analysis of computer code outputs: A tutorial. Reliab Eng Sys Safe 2006;91:1290-300
99. Kennedy MC, O'Hagan A. Bayesian calibration of computer models (with discussion). J R Stat Soc, Series B 2001;63:425-64
100. Goldstein M, Rougier J. Bayes linear calibrated prediction for complex systems. J Am Stat Assoc 2006; 101:1132-43
101. Challenor PG, Hankin RKS, Marsh R. Towards the probability of rapid climate change. In: Schellnhuber HJ, Cramer W, Nakicenovic N, et al. (eds). Avoiding Dangerous Climate Change. Cambridge: Cambridge University Press, 2006, 53-63
102. Wilkinson DJ. Parallel Bayesian computation. In: Kontoghiorghes EJ, (ed), Handbook of Parallel Computing and Statistics. New York: Marcel Dekker/CRC Press, 2005, 481-512