1.
Bondeson, D. P., Mares, A., Smith, I. E., et al. Catalytic In Vivo Protein Knockdown by Small-Molecule PROTACs. Nat. Chem. Biol. 2015, 11, 611–617.
Google Scholar |
Crossref |
Medline2.
Douglass, E. F., Miller, C. J., Sparer, G. A Comprehensive Mathematical Model for Three-Body Binding Equilibria. J. Am. Chem. Soc. 2013, 135, 6092–6099.
Google Scholar |
Crossref |
Medline3.
Haas, C. N., Rose, J. B., Gerba, C. P. Quantitative Microbial Risk Assessment. John Wiley & Sons: New York, 1999.
Google Scholar4.
Hill, A. V. The Possible Effects of the Aggregation of the Molecules of Haemoglobin on Its Dissociation Curves. J. Physiol. 1910, 40, 4–7.
Google Scholar5.
Kimeldorf, G. S., Wahba, G. A Correspondence between Bayesian Estimation on Stochastic Processes and Smoothing by Splines. Ann. Math. Stat. 1970, 41, 495–502.
Google Scholar |
Crossref6.
Shockley, K. R. Estimating Potency in High-Throughput Screening Experiments by Maximizing the Rate of Change in Weighted Shannon Entropy. Sci. Rep. 2016, 6, 27897.
Google Scholar |
Crossref |
Medline7.
Gould, A. L. BMA-Mod: A Bayesian Model Averaging Strategy for Determining Dose-Response Relationships in the Presence of Model Uncertainty. Biom. J. 2019, 61, 1141–1159.
Google Scholar |
Crossref |
Medline8.
Levenberg, K. A Method for the Solution of Certain Non-Linear Problems in Least Squares. Q. Appl. Math. 1944, 2, 164–168.
Google Scholar |
Crossref9.
Hatherell, S., Baltazar, M. T., Reynolds, J., et al. Identifying and Characterizing Stress Pathways of Concern for Consumer Safety in Next-Generation Risk Assessment. Toxicol. Sci. 2020, 176, 11-33.
Google Scholar |
Crossref |
Medline10.
Steinruecken, C., Smith, E, Janz, D., et al. The Automatic Statistician. In Automated Machine Learning. Springer: Cham, 2019; pp 161–173.
Google Scholar |
Crossref11.
Görtler, J., Kehlbeck, R., Deussen, O. A Visual Exploration of Gaussian Processes. Distill 2019, 4, e17.
Google Scholar |
Crossref12.
Juárez, M., Steel, M. Model-Based Clustering of Non-Gaussian Panel Data Based on Skew-t Distributions. J. Bus. Econ. Stat. 2010, 28, 52–66.
Google Scholar |
Crossref13.
Carpenter, B., Gelman, A., Hoffman, M. D., et al. Stan: A Probabilistic Programming Language. J. Stat. Softw. 2017, 76, 1–32.
Google Scholar |
Crossref14.
RStudio Team . RStudio: Integrated Development Environment for R.
http://www.rstudio.com/ (accessed July 15, 2021).
Google Scholar15.
Gabry, J., Mahr, T. bayesplot: Plotting for Bayesian Models. R Package. Version 1.6.0.
https://mc-stan.org/bayesplot/ (accessed July 15, 2021).
Google Scholar16.
Hespanhol, L., Vallio, C. S., Costa, L. M., et al. Understanding and Interpreting Confidence and Credible Intervals around Effect Estimates. Braz. J. Phys. Ther. 2019, 23, 290–301.
Google Scholar |
Crossref |
Medline17.
Lazic, S. E. Ranking, Selecting, and Prioritising Genes with Desirability Functions. PeerJ 2015, 3, e1444.
Google Scholar |
Crossref |
Medline18.
Reynolds, J., Malcomber, S., White, A. A Bayesian Approach for Inferring Global Points of Departure from Transcriptomics Data. Comput. Toxicol. 2020, 16, 100138.
Google Scholar |
Crossref19.
Ritz, C., Baty, F., Streibig, J. C., et al. Dose-Response Analysis Using R. PLoS One 2015, 10, e0146021.
Google Scholar |
Crossref |
Medline
留言 (0)