Bayesian methods in bioinformatics and computational systems biology
Open accessAbstract
Bayesian methods are valuable, inter alia, whenever there is a need to extract information from data that are uncertain or subject to any kind of error or noise (including measurement error and experimental error, as well as noise or random variation intrinsic to the process of interest). Bayesian methods offer a number of advantages over more conventional statistical techniques that make them particularly appropriate for complex data. It is therefore no surprise that Bayesian methods are becoming more widely used in the fields of genetics, genomics, bioinformatics and computational systems biology, where making sense of complex noisy data is the norm. This review provides an introduction to the growing literature in this area, with particular emphasis on recent developments in Bayesian bioinformatics relevant to computational systems biology.
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MCMC and related techniques made feasible many complex computations necessary in Bayesian methods, and Bayesian theory resurfaced and started to be applied in many areas, including bioinformatics and computational systems biology.
“MCMC and related techniques [39,40] made feasible many of the complex computations necessary in Bayesian methods and the theory resurfaced and started to be applied in many areas [41], including bioinformatics and computational systems biology [42–44].”
From Reverse engineering and identification in systems biology: strategies, perspectives and challenges (2014) · cites this paper as [42]
Bayesian methods are especially useful to extract information from uncertain and noisy data, the most common scenario in bioinformatics and computational systems biology.
“Bayesian methods, which are especially useful to extract information from uncertain and noisy data (the most common scenario in bioinformatics and computational systems biology), started to receive greater attention [42,44,185].”
From Reverse engineering and identification in systems biology: strategies, perspectives and challenges (2014) · cites this paper as [42]
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Cites
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192 · see citing works
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) · doi:10.1201/b16018 · Cited by 27,276 · see citing works
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949 · see citing works
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195 · see citing works
Sequential Monte Carlo Methods in Practice
Doucet, A. (2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9 · Cited by 7,445 · see citing works
Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination
Green, P. J. (1995) · Biometrika · doi:10.1093/biomet/82.4.711 · Cited by 5,941 · see citing works
Biological sequence analysis probabilistic models of proteins and nucleic acids
Durbin, R. M., Eddy, S. R., Krogh, A., & Mitchison, G. (1998) · Cited by 3,565 · see citing works
Markov Chains for Exploring Posterior Distributions
Tierney, L. (1994) · The Annals of Statistics · doi:10.1214/aos/1176325750 · Cited by 3,513 · see citing works
Using Bayesian Networks to Analyze Expression Data
Friedman, N., Michal Linial, B., Nachman, I., & Pe’er, D. (2000) · Journal of Computational Biology · doi:10.1089/106652700750050961 · Cited by 3,248 · see citing works
Casella, G., & George, E. I. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475878 · Cited by 2,424 · see citing works
Computational systems biology.
Kitano, H. (2002) · Nature · doi:10.1038/nature01254 · Cited by 2,209 · see citing works
Practical markov chain monte carlo
Geyer, C. J. (1992) · Statistical Science · doi:10.1214/ss/1177011137 · Cited by 2,045 · see citing works
Stochastic mechanisms in gene expression.
McAdams, H., & Arkin, A. (1997) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.94.3.814 · Cited by 1,970 · see citing works
Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
Sachs, K., Perez, O. D., Pe’er, D., Lauffenburger, D. A., & Nolan, G. P. (2005) · Science · doi:10.1126/science.1105809 · Cited by 1,724 · see citing works
Huber, P. J. (1997) · Springer series in statistics · doi:10.1007/978-1-4612-0667-5_6 · Cited by 1,556 · see citing works
Arkin, A. P., Ross, J., & McAdams, H. H. (1998) · Genetics · doi:10.1093/genetics/149.4.1633 · Cited by 1,492 · see citing works
The chemical Langevin equation
Gillespie, D. T. (2000) · The Journal of Chemical Physics · doi:10.1063/1.481811 · Cited by 1,475 · see citing works
Predicting the clinical status of human breast cancer by using gene expression profiles
West, M., Blanchette, C., Dressman, H. K., Huang, E. S., Ishida, S., Spang, R., Zuzan, H., Olson, J. A., Marks, J. R., & Nevins, J. R. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.201162998 · Cited by 1,318 · see citing works
A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data
Jansen, R., Yu, H., Greenbaum, D. S., Kluger, Y., Krogan, N. J., Chung, S., Emili, A., Snyder, M. P., Greenblatt, J. F., & Gerstein, M. (2003) · Science · doi:10.1126/science.1087361 · Cited by 1,254 · see citing works
Inferring Cellular Networks Using Probabilistic Graphical Models
Friedman, N. (2004) · Science · doi:10.1126/science.1094068 · Cited by 1,208 · see citing works
Smith, J. Q., Bernardo, J. M., Berger, J. O., Dawid, A. P., & Smith, A. F. M. (1993) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2982740 · Cited by 1,098 · see citing works
Stochastic Modelling for Systems Biology
Wilkinson, D. (2006) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420010664 · Cited by 1,041 · see citing works
Self-citationModel-based clustering and data transformations for gene expression data
Yeung, K. Y., Fraley, C., Murua, A., Raftery, A. E., & Ruzzo, W. L. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.10.977 · Cited by 869 · see citing works
Parameter estimation in biochemical pathways: a comparison of global optimization methods
Moles, C. G., Mendes, P., & Banga, J. R. (2003) · Genome Research · doi:10.1101/gr.1262503 · Cited by 858 · see citing works
Markov chain Monte Carlo method and its application
Brooks, S. (1998) · Journal of the Royal Statistical Society: Series D (The Statistician) · doi:10.1111/1467-9884.00117 · Cited by 843 · see citing works
Bayesian analysis of computer code outputs: A tutorial
O’Hagan, A. (2006) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2005.11.025 · Cited by 794 · see citing works
An empirical Bayes approach to inferring large-scale gene association networks
Schäfer, J., & Strimmer, K. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bti062 · Cited by 786 · see citing works
Gilks, W. R. (2005) · Encyclopedia of Biostatistics · doi:10.1002/0470011815.b2a14021 · Cited by 711 · see citing works
A Probabilistic Functional Network of Yeast Genes
Lee, I., Date, S. V., Adai, A. T., & Marcotte, E. M. (2004) · Science · doi:10.1126/science.1099511 · Cited by 682 · see citing works
Yu, J., Smith, V. A., Wang, P. P., Hartemink, A. J., & Jarvis, E. D. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth448 · Cited by 675 · see citing works
Zhang, W., & Chait, B. T. (2000) · Analytical Chemistry · doi:10.1021/ac991363o · Cited by 594 · see citing works
Statistical Analysis of Gene Expression Microarray Data
Speed, T. (2003) · Chapman & Hall/CRC eBooks · doi:10.1201/9780203011232 · Cited by 551 · see citing works
Husmeier, D. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg313 · Cited by 535 · see citing works
Avoiding dangerous climate change
Challinor, A. J., Wheeler, T., Osborne, T., & Slingo, J. M. (2006) · Cambridge University Press eBooks · Cited by 533 · see citing works
Ronen, M., Rosenberg, R., Shraiman, B. I., & Alon, U. S. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.152046799 · Cited by 532 · see citing works
The Bayesian revolution in genetics
Beaumont, M., & Rannala, B. H. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1318 · Cited by 521 · see citing works
Sparse graphical models for exploring gene expression data
Dobra, A., Hans, C., Jones, B., et al. (2004) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2004.02.009 · Cited by 462 · see citing works
High-throughput fluorescence microscopy for systems biology
Pepperkok, R., & Ellenberg, J. (2006) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm1979 · Cited by 412 · see citing works
Statistical mechanical approaches to models with many poorly known parameters.
Brown, K. S., & Sethna, J. P. (2003) · Physical Review E · doi:10.1103/physreve.68.021904 · Cited by 398 · see citing works
Stochastic models for heterogeneous DNA sequences
Churchill, G. A. (1989) · Bulletin of Mathematical Biology · doi:10.1007/bf02458837 · Cited by 393 · see citing works
Werhli, A. V., Grzegorczyk, M. A., & Husmeier, D. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl391 · Cited by 338 · see citing works
Bayesian Models for Multiple Local Sequence Alignment and Gibbs Sampling Strategies
Liu, J. S., Neuwald, A. F., & Lawrence, C. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476622 · Cited by 300 · see citing works
Decomposable graphical Gaussian model determination
Giudici, P. (1999) · Biometrika · doi:10.1093/biomet/86.4.785 · Cited by 263 · see citing works
Bayesian inference for a discretely observed stochastic kinetic model
Boys, R. J., Wilkinson, D. J., & Kirkwood, T. B. L. (2007) · Statistics and Computing · doi:10.1007/s11222-007-9043-x · Cited by 252 · see citing works
Self-citationExperiments in Stochastic Computation for High-Dimensional Graphical Models
Jones, B., Carvalho, C. M., Dobra, A., et al. (2005) · Statistical Science · doi:10.1214/088342305000000304 · Cited by 248 · see citing works
Linking data to models: data regression
Jaqaman, K., & Danuser, G. (2006) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2030 · Cited by 243 · see citing works
Bayesian coestimation of phylogeny and sequence alignment
Lunter, G. A., Miklós, I., Drummond, A. J., Jensen, J. L., & Hein, J. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-83 · Cited by 195 · see citing works
Inferring quantitative models of regulatory networks from expression data
Nachman, I., Regev, A., & Friedman, N. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth941 · Cited by 193 · see citing works
Bayesian inference for nonlinear multivariate diffusion models observed with error
Golightly, A., & Wilkinson, D. J. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.05.019 · Cited by 191 · see citing works
Self-citationStatistics for Microarrays : Design, Analysis and Inference
Wit, E. C., & McClure, J. (2004) · Lancaster EPrints (Lancaster University) · Cited by 183 · see citing works
Bayesian Inference for Stochastic Kinetic Models Using a Diffusion Approximation
Golightly, A., & Wilkinson, D. J. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00345.x · Cited by 183 · see citing works
Self-citationBernard, A., & Hartemink, A. J. (2004) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812702456_0044 · Cited by 173 · see citing works
Bayesian inference on biopolymer models.
Liu, J. S., & Lawrence, C. E. (1999) · Bioinformatics · doi:10.1093/bioinformatics/15.1.38 · Cited by 164 · see citing works
Embracing the complexity of genomic data for personalized medicinePreprint
West, M., Ginsburg, G. S., Huang, A. T., & Nevins, J. R. (2006) · Genome Research · doi:10.1101/gr.3851306 · Cited by 155 · see citing works
Iterative approach to model identification of biological networks
Gadkar, K. G., Gunawan, R., & Doyle, F. J. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-155 · Cited by 154 · see citing works
Opgen-Rhein, R., & Strimmer, K. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-s2-s3 · Cited by 150 · see citing works
Bayesian Segmentation of Protein Secondary Structure
Schmidler, S. C., Liu, J. S., & Brutlag, D. L. (2000) · Journal of Computational Biology · doi:10.1089/10665270050081496 · Cited by 148 · see citing works
Maximum likelihood alignment of DNA sequences
Bishop, M. J., & Thompson, E. A. (1986) · Journal of Molecular Biology · doi:10.1016/0022-2836(86)90289-5 · Cited by 148 · see citing works
Bayesian Models for Gene Expression With DNA Microarray Data
Ibrahim, J. G., Chen, M., & Gray, R. J. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502753479257 · Cited by 143 · see citing works
Bayesian sequential inference for nonlinear multivariate diffusions
Golightly, A., & Wilkinson, D. J. (2006) · Statistics and Computing · doi:10.1007/s11222-006-9392-x · Cited by 138 · see citing works
Self-citationRanked prediction of p53 targets using hidden variable dynamic modeling
Barenco, M., Daniela, T., Brewer, D. S., Callard, R. E., Stark, J., & Hubank, M. J. F. (2006) · Genome biology · doi:10.1186/gb-2006-7-3-r25 · Cited by 126 · see citing works
Parameter estimation in stochastic biochemical reactions
Reinker, S., Altman, R. M., & Timmer, J. (2006) · Systems Biology · doi:10.1049/ip-syb:20050105 · Cited by 109 · see citing works
Bayesian alignment using hierarchical models, with applications in protein bioinformatics
Green, P. J., & Mardia, K. V. (2006) · Biometrika · doi:10.1093/biomet/93.2.235 · Cited by 102 · see citing works
Bayesian Inference for Gene Expression and Proteomics
Vannucci, M. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511584589 · Cited by 94 · see citing works
Bayesian Robust Inference for Differential Gene Expression in Microarrays with Multiple Samples
Gottardo, R., Raftery, A. E., Yeung, K. Y., & Bumgarner, R. E. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00397.x · Cited by 94 · see citing works
Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models
Golightly, A., & Wilkinson, D. J. (2006) · Journal of Computational Biology · doi:10.1089/cmb.2006.13.838 · Cited by 90 · see citing works
Self-citationBayes Linear Calibrated Prediction for Complex Systems
Goldstein, M., & Rougier, J. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000203 · Cited by 85 · see citing works
Wilkinson, D. J. (2005) · Statistics, a series of textbooks and monographs · doi:10.1201/9781420028683.ch16 · Cited by 84 · see citing works
Self-citationDetecting Homogeneous Segments in DNA Sequences by Using Hidden Markov Models
Boys, R. J., Henderson, D. A., & Wilkinson, D. J. (2000) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/1467-9876.00191 · Cited by 83 · see citing works
Self-citationReconstruction of gene networks using Bayesian learning and manipulation experiments
Pournara, I., & Wernisch, L. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth337 · Cited by 80 · see citing works
Markovian Structures in Biological Sequence Alignments
Liu, J. S., Neuwald, A. F., & Lawrence, C. E. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10473814 · Cited by 72 · see citing works
Statistical analysis of microarray data: a Bayesian approach
Gottardo, R. (2003) · Biostatistics · doi:10.1093/biostatistics/4.4.597 · Cited by 72 · see citing works
Bayesian Modeling of Differential Gene Expression
Lewin, A., Richardson, S. T., Marshall, C., Glazier, A. M., & Aitman, T. J. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00394.x · Cited by 67 · see citing works
Heard, N. A., Holmes, C. C., Stephens, D. A., Hand, D. J., & Dimopoulos, G. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0408393102 · Cited by 66 · see citing works
Biochemical networks with uncertain parameters
Liebermeister, W., & Klipp, E. (2005) · Systems Biology · doi:10.1049/ip-syb:20045033 · Cited by 63 · see citing works
A novel approach for clustering proteomics data using Bayesian fast Fourier transform
Bensmail, H., Golek, J., Moody, M. M., Semmes, J. O., & Haoudi, A. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti383 · Cited by 57 · see citing works
Informative priors based on transcription factor structural class improve de novo motif discovery
Narlikar, L., Gordân, R., Ohler, U., et al. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl251 · Cited by 55 · see citing works
BGX: a fully Bayesian integrated approach to the analysis of Affymetrix GeneChip data
Hein, A.-M. K. (2005) · Biostatistics · doi:10.1093/biostatistics/kxi016 · Cited by 52 · see citing works
Bayesian models for pooling microarray studies with multiple sources of replications
Conlon, E. M., Song, J. J., & Liu, J. S. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-247 · Cited by 49 · see citing works
A Probabilistic Model for Cell Cycle Distributions in Synchrony Experiments
Orlando, D. A., Lin, C. Y., Bernard, A., et al. (2007) · Cell Cycle · doi:10.4161/cc.6.4.3859 · Cited by 47 · see citing works
Genome-wide estimation of transcript concentrations from spotted cDNA microarray data
Frigessi, A. (2005) · Nucleic Acids Research · doi:10.1093/nar/gni141 · Cited by 37 · see citing works
Probabilistic segmentation and intensity estimation for microarray images
Gottardo, R., Besag, J. E., Stephens, M., et al. (2005) · Biostatistics · doi:10.1093/biostatistics/kxi042 · Cited by 30 · see citing works
Towards the probability of rapid climate change
Challenor, P., Hankin, R. K. S., & Marsh, R. (2006) · ePrints Soton (University of Southampton) · Cited by 28 · see citing works
Bayesian integrated functional analysis of microarray data
Bhattacharjee, M., Pritchard, C. C., Nelson, P. S., et al. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth338 · Cited by 23 · see citing works
A stochastic model of gene transcription: An application to L1 retrotransposition events
Rempała, G. A., Ramos, K. S., & Kalbfleisch, T. (2006) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2006.02.010 · Cited by 22 · see citing works
Nonparametric Models for Proteomic Peak Identification and Quantification
Clyde, M. A., House, L., & Wolpert, R. L. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511584589.016 · Cited by 16 · see citing works
Fast Bayesian Shape Matching Using Geometric Algorithms
Schmidler, S. C. (2007) · doi:10.1093/oso/9780199214655.003.0018 · Cited by 15 · see citing works
Bayesian Normalization and Identification for Differential Gene Expression Data
Zhang, D., Wells, M. T., Smart, C. D., et al. (2005) · Journal of Computational Biology · doi:10.1089/cmb.2005.12.391 · Cited by 15 · see citing works
Improving Mass and Liquid Chromatography Based Identification of Proteins Using Bayesian Scoring
Chen, S. S., Deutsch, E. W., Yi, E. C., et al. (2005) · Journal of Proteome Research · doi:10.1021/pr050251c · Cited by 14 · see citing works
Analysis of Mass Spectrometry Data Using Bayesian Wavelet-Based Functional Mixed Models
Morris, J. S., Brown, P. J., Baggerly, K., et al. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511584589.015 · Cited by 13 · see citing works
Bayesian refinement of protein functional site matching
Mardia, K. V., Nyirongo, V. B., Green, P. J., et al. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-257 · Cited by 12 · see citing works
Single cell resolution in regulation of gene expression
Bahcall, O. G. (2005) · Molecular Systems Biology · doi:10.1038/msb4100020 · Cited by 12 · see citing works
Bayesian Methods in Biological Sequence Analysis
Liu, J. S., & Logvinenko, T. (2003) · doi:10.1002/0470022620.bbc03 · Cited by 6 · see citing works
Altman, R., Dunker, A. K., Hunter, L. M., et al. (2003) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 2 · see citing works
van de Wiel, M. A., Holden, M., Glad, I. K., et al. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511584589.005 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 13
- [2]2. O'Hagan A, Forster JJ. Bayesian Inference. In: Kendall' s AdvancedTheory of Statistics , Vol. 2B, London: Arnold, 2004
- [6]6. Lauritzen SL. Graphical Models . Oxford: Oxford Science Publications, 1996
- [19]19. Zhou Q, Liu JS. Modelling within-motif dependence for transcription factor binding site predictions. Bioinformatics 2004; 20 :909-16
- [25]25. Boys RJ, Henderson DA. A Bayesian approach to DNA sequence segmentation (with discussion). Biometrics 2004; 60 :573-88
- [22]22. In: Vanucci M, Do K-A, Mu ¨ller P, (eds). Bayesian Inference for Gene Expression and Proteomics . Cambridge University Press, New York, 2006, 75-96
- [38]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
- [44]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]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
- [54]54. Saksena A, Lucarelli D, Wang I-J. Bayesian model selection for mining mass spectrometry data. Neural Netw 2005; 18 :843-9
- [20]20. selection procedure for the vector autoregressive process. Bioformatics 2007;8: PMSB06 supplement, in press
- [84]84. McAdams HH, Arkin A. It's a noisy business: genetic regulation at the nanomolecular scale. Trends Genet 1999; 15 :65-9
- [87]87. Shen H, Nelson G, Nelson DE, et al . Automated tracking of gene expression profiles in individual cells and cell compartments. J RSoc Interface 2006;3:787-794
- [99]99. Kennedy MC, O'Hagan A. Bayesian calibration of computer models (with discussion). J R Stat Soc, Series B 2001; 63 :425-64
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“…several experiment design methods have been successfully developed including classical Fisher information matrix (FIM) design (Bandara et al., 2009), Bayesian methods (Chaloner and Verdinelli, 1995; Kramer and Radde, 2010; Wilkinson, 2007) and optimization-based methods (Kreutz et al., 2011).”
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- Authors
- Wilkinson, D. J.
- Affiliations
- Newcastle University (GB)
- Corresponding author
- Wilkinson, D. J.
- ORCID
- Wilkinson, D. J.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-12-19
- Volume
- 8
- Issue
- 2
- Pages
- 109-116
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W2112794562
- Cited by
- 230
- References
- 95
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- FWCI
- 0.42
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Briefings in Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Newcastle University ePrints (Newcastle Univesity)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- Bayesian analysis, computational systems biology, noisy data, measurement error, uncertainty quantification, genomics, complex data analysis
- MeSH terms
- Artificial Intelligence, Bayes Theorem, Pattern Recognition, Automated, Signal Transduction, Sequence Analysis, Computational Biology, Oligonucleotide Array Sequence Analysis, Proteome, Gene Expression Profiling, Systems Biology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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