Aggarwal, K. K., Singh, Y., Chandra, P. & Puri, M. (2005). Bayesian regularization in a neural network model to estimate lines of code using function points. Journal of Computer Sciences 1, 505–509.
Alados, I., Mellado, J. A., Ramos, F. & Alados-Arboledas, L. (2004). Estimating UV erythema1 irradiance by means of neural networks. Photochemistry and Photobiology 80, 351–358.
Bishop, C. M. & Tipping, M. E. (1998). A hierarchical latent variable model for data visualization. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 281–293.
Chen, L. J., Cui, L. Y., Xing, L. & Han, L. J. (2008). Prediction of the nutrient content in dairy manure using artificial neural network modeling. Journal of Dairy Science 91, 4822–4829.
Curtis, D. (2007). Comparison of artificial neural network analysis with other multimarker methods for detecting genetic association. BMC Genetics 8, 49.
de los Campos, G., Hugo, N., Gianola, G., Crossa, J., Legarra, A., Manfredi, E., Weigel, K. & Miguel, C. J. (2009). Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182, 375–385.
Demuth, H., Beale, M. & Hagan, M. (2009). Neural Network Toolbox™ 6 User's Guide. The MathWorks Inc. Natick, MA, USA.
Feng, N., Wang, F. & Qiu, Y. (2006). Novel approach for promoting the generalization ability of neural networks. International Journal of Signal Processing 2, 131–135.
Fernandez, M. & Caballero, J. (2006). Ensembles of Bayesian-regularized genetic neural networks for modeling of acetylcholinesterase inhibition by huprines. Chemistry and Biology Drug Design 68, 201–212.
Foresee, F. D. &. Hagan, M. T. (1997). Gauss-Newton approximation to Bayesian learning. In Proceedings of IEEE International Conference on Neural Networks 1997 (ed. Hagan, M. T.), pp. 1930–1935.
Forshed, J., Anderson, O. F. & Jacobsson, P. S. (2002). NMR and Bayesian regularized neural network regression for impurity determination of 4-aminophenol. Journal of Pharmaceutical and Biomedical Analysis 29, 495–505.
Gencay, R. & Qi, M. (2001). Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging. IEEE Transactions on Neural Networks 12, 726–734.
Guha, R., Stanton, T. D. & Jurs, C. P. (2005). Interpreting computational neural network quantitative structure-activity relationship models: a detailed interpretation of the weights and biases. Journal of Chemical Information and Modelling 45, 109–1121.
Hajmeer, M., Basheer, I. & Cliver, D. O. (2006). Survival curves of Listeria monocytogenes in chorizos modeled with artificial neural networks. Food Microbiology 23, 561–70.
Haykin, S. (2008). Neural Networks: Comprehensive Foundation. 2nd edn. Upper Saddle River, NJ: Prentice-Hall. A Comprehensive Foundation 3rd edit: Prentice-Hall.
Joseph, H., Huang, W. L. & Dickman, M. (2003). Neural network modelling of coastal algal blooms. Ecology Modelling 159, 179–201.
Kelemen, A. & Liang, Y. (2008). Statistical advances and challenges for analyzing correlated high dimensional SNP data in genomic study for complex. Diseases Statistics Surveys 2, 43–60.
Kumar, P., Merchant, S. N. & Desai, U. B. (2004). Improving performance in pulse radar detection using Bayesian regularization for neural network training. Digital Signal Processing 14, 438–448.
Lampinen, J. & Vehtari, A. (2001). Bayesian approach for neural networks review and case studies. Neural Networks 14, 257–274.
Legarra, A., Robert-Granie, C., Manfredi, E. & Elsen, J. M. (2008). Performance of genomic selection in mice. Genetics 180, 611–618.
Long, N., Gianola, D., Rosa, G. J. M., Weigel, K. A. & Avendan, S. (2007). Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers. Journal of Animal Breeding and Genetics 124, 377–389.
MacKay, D. J. C. (1992). Bayesian interpolation. Neural Computation 4, 415–447.
MacKay, J. C. D. (1996). Comparison of approximate methods for handling hyperparameters. Neural Computation 8, 1–35.
MacKay, J. C. D. (2008). Information theory, inference and learning algorithms. Cambridge: Cambridge University Press.
Maier, H. R. & Dandy, C. G. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling and Software 15, 101–124.
Marwala, T. (2007). Bayesian training of neural networks using genetic programming. Pattern Recognition Letters 28, 1452–1458.
Mott, R. (2006). Finding the molecular basis of complex genetic variation in humans and mice. Philosophical Transactions 361, 393–401.
Mott, R., Talbot, C. J., Turri, M. G., Collins, A. C. & Flint, J. (2000). A method for fine mapping quantitative trait loci in outbred animal stocks. Proceedings of the National Academy of Sciences of the USA 97, 12649–12654.
Mutoh, H., Hamajima, N., Tajima, K., Kobayahsi, T. & Honda, H. (2005). Exhaustive exploring using artificial neural network for identification of SNPs combination to Heliobacter pylori infection susceptibility. Chem-Bio Informatics 5, 15–26.
Nguyen, D. & Widrow, B. (1990). Improving the learning speed of two-layer neural networks by choosing initial values of the adaptive weights. Proceedings of International Joint Conference on Neural Networks 3, 21–26.
Ping, G., Michael, R. L. & Chen, C. L. P. (2003). Regularization Parameter Estimation for Feedforward Neural Networks. IEEE Transactions of Systems, Man, and Cybernetics—Part B: Cybernetics 33, 35–44.
Ripley, B. D. (2007). Pattern Recognition and Neural Networks. New York: Cambridge University Press.
SAS/STAT® (2009). Version 9.13. Cary, NC: SAS Institute Inc.
Shaneh, A. & Butler, G. (2006). Bayesian learning for feed-forward neural network with application to proteomic data: the glycosylation sites detection of the epidermal growth factor-like proteins associated with cancer as a case study. In Canadian AI LNAI 4013, 2006 (ed. Lamontagne, L. & Marchand, M.), pp. 110–121. Berlin-Heiddelberg: Springer-Verleg.
Sorich, M. J., Miners, J. O., Ross, A. M., Winker, D. A., Burden, F. R. & Smith, P. A. (2003). Comparison of linear and nonlinear classification algorithms for the prediction of drug and chemical metabolism by human UDP-Glucuronosyltransferase isoforms. Journal of Chemical Information and Computer Sciences 43, 2019–2024.
Thodberg, H. H. (1996). A review of Bayesian neural networks with an application to near infrared spectroscopy. IEEE Transactions on Neural Networks 7, 56–72.
Titterington, D. M. (2004). Bayesian methods for neural networks and related models. Statistical Science 19, 128–139.
Tu, J. V. (1997). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology 49, 1225–1231.
Useche, F., Hanafey, G. G. & Rafalski, A. (2001). High-throughput identification, database storage and analysis of SNPs in EST sequences. Genome Informatics 12, 194–203.
Valdar, W., Solberg, L. C., Gauguier, D., Burnett, S. & Klenerman, P. (2006 a). Genome-wide genetic association of complex traits in heterogeneous stock mice. Nature Genetics 38, 879–887.
Valdar, W., Solberg, L. C., Gauguier, D., Cookson, W. O. & Rawlins, J. N. P. (2006 b). Genetic and environmental effects on complex traits in mice. Genetics 174, 959–984.
Vazquez, A. I., Rosa, G. J. M., Weigel, K. A., de los Campos, G., Gianola, G. & Allison, D. B. (2010). Predictive ability of subsets of single nucleotide polymorphisms with and without parent average in US Holsteins. Journal of Dairy Sciences 93(12), 5942–5949.
Wang, H. J., Ji, F., Leung, C. S. & Sum, P. F. (2009). Regularization parameter selection for faulty neural networks. International Journal of Intelligent Systems and Technologies 4, 45–48.
Winkler, D. A. & Burden, F. R. (2004). Modelling blood–brain barrier partitioning using Bayesian neural nets. Journal of Molecular Graphics and Modelling 22, 499–505.
Xu, M., Zengi, G., Xu, X., Huang, G., Jiang, R. & Sun, W. (2006). Application of Bayesian regularized BP neural network model for trend analysis, acidity and chemical composition of precipitation in North. Water, Air, and Soil Pollution 172, 167–184.