Cloud Hosting

Khanna, I. Drug discovery in pharmaceutical industry: productivity challenges and trends. Drug Discov. Today 17, 10881102 (2012).

Article Google Scholar

Sarkis, M., Bernardi, A., Shah, N. & Papathanasiou, M. M. Emerging challenges and opportunities in pharmaceutical manufacturing and distribution. Processes 9, 457 (2021).

Article Google Scholar

Zhuang, W., Hachem, K., Bokov, D., Ansari, M. J. & Nakhjiri, A. T. Ionic liquids in pharmaceutical industry: A systematic review on applications and future perspectives. J. Mol. Liquids 349, 118145 (2021).

Article Google Scholar

Birmingham, B. & Buvanendran, A. 40 - Nonsteroidal Anti-inflammatory Drugs, Acetaminophen, and COX-2 Inhibitors. In Practical Management of Pain (Fifth Edition) (eds Benzon, H. T. et al.) 553-568.e555 (Mosby, 2014).

Chapter Google Scholar

Dallegri, F., Bertolotto, M. & Ottonello, L. A review of the emerging profile of the anti-inflammatory drug oxaprozin. Expert Opin. Pharmacother. 6, 777785 (2005).

CAS Article Google Scholar

Todd, P. A. & Brogden, R. N. Oxaprozin: A preliminary review of its pharmacodynamic and pharmacokinetic properties, and therapeutic efficacy. Drugs 32, 291312 (1986).

CAS Article Google Scholar

Miller, L. Oxaprozin: A once-daily nonsteroidal anti-inflammatory drug. Clin. Pharm. 11, 591603 (1992).

CAS PubMed Google Scholar

Hojjati, M., Yamini, Y., Khajeh, M. & Vatanara, A. Solubility of some statin drugs in supercritical carbon dioxide and representing the solute solubility data with several density-based correlations. J. Supercrit. Fluids 41, 187194 (2007).

CAS Article Google Scholar

Foster, N. et al. Processing pharmaceutical compounds using dense gas technology. Ind. Eng. Chem. Res. 42, 64766493 (2003).

CAS Article Google Scholar

Gl-stnda, . & Temelli, F. Solubility behavior of ternary systems of lipids, cosolvents and supercritical carbon dioxide and processing aspects. J. Supercrit. Fluids 36, 115 (2005).

Article Google Scholar

Paul, D. et al. Artificial intelligence in drug discovery and development. Drug Discov. Today 26, 80 (2021).

CAS Article Google Scholar

Yang, J., Du, Q., Ma, R. & Khan, A. Artificial intelligence simulation of water treatment using a novel bimodal micromesoporous nanocomposite. J. Mol. Liq. 340, 117296 (2021).

CAS Article Google Scholar

El Naqa, I. & Murphy, M.J. What is machine learning?. In: Machine Learning in Radiation Oncology. 311 (Springer, 2015).

Wang, H., Lei, Z., Zhang, X., Zhou, B. & Peng, J. Machine learning basics. Deep Learn. 98164 (2016).

Dietterich, T.G. Ensemble methods in machine learning. In: International workshop on multiple classifier systems. 115 (Springer, 2000).

Zhou, Z.-H. Ensemble methods: Foundations and algorithms, Chapman and Hall/CRC, 2019.

Freund, Y. & Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119139 (1997).

MathSciNet Article Google Scholar

Mathuria, M. Decision tree analysis on j48 algorithm for data mining. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3, 11141119 (2013).

Google Scholar

Sakar, A. & Mammone, R. J. Growing and pruning neural tree networks. IEEE Trans. Comput. 42, 291299 (1993).

Article Google Scholar

Frau, L., Susto, G. A., Barbariol, T. & Feltresi, E. Uncertainty estimation for machine learning models in multiphase flow applications. Informatics. 8, 58 (2021).

Article Google Scholar

Mosavi, A. et al. susceptibility mapping of soil water erosion using machine learning models. Water 12, 1995 (2020).

Article Google Scholar

Khoshmaram, A. et al. Supercritical process for preparation of nanomedicine: Oxaprozin case study. Chem. Eng. Technol. 44, 208212 (2021).

CAS Article Google Scholar

Quinonero-Candela, J. & Rasmussen, C. E. A unifying view of sparse approximate Gaussian process regression. J. Mach. Learn. Res. 6, 19391959 (2005).

MathSciNet MATH Google Scholar

Jiang, Y., Jia, J., Li, Y., Kou, Y. & Sun, S. Prediction of gas-liquid two-phase choke flow using Gaussian process regression. Flow Meas. Instrum. 81, 102044 (2021).

Article Google Scholar

Quinlan, J. R. Learning decision tree classifiers. ACM Comput. Surv. (CSUR) 28, 7172 (1996).

Article Google Scholar

Xu, M., Watanachaturaporn, P., Varshney, P. K. & Arora, M. K. Decision tree regression for soft classification of remote sensing data. Remote Sens. Environ. 97, 322336 (2005).

ADS Article Google Scholar

Kushwah, J. S. et al. Comparative study of regressor and classifier with decision tree using modern tools. Mater. Today Proc. 56, 35713576 (2021).

Article Google Scholar

Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. Classification and regression trees. (Routledge, 2017).

Segal, M. R. & Bloch, D. A. A comparison of estimated proportional hazards models and regression trees. Stat. Med. 8, 539550 (1989).

CAS Article Google Scholar

Schapire, R.E. The boosting approach to machine learning: An overview. Nonlinear estimation and classification 149171 (2003).

Ying, C., Qi-Guang, M., Jia-Chen, L. & Lin, G. Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica 39, 745758 (2013).

Article Google Scholar

Hastie, T., Tibshirani, R. & Friedman, J. The elements of statistical learning. Springer series in statistics (Springer, 2001).

Bishop, C. M. & Nasrabadi, N. M. Pattern recognition and machine learning Vol. 4. (New York: springer, 2006).

Hastie, T., Rosset, S., Zhu, J. & Zou, H. Multi-class adaboost, statistics and its. Interface 2, 349360 (2009).

MathSciNet MATH Google Scholar

Berk, R. A. An introduction to ensemble methods for data analysis. Sociol. Methods Res. 34, 263295 (2006).

MathSciNet Article Google Scholar

Ouyang, Z., Ravier, P. & Jabloun, M. STL decomposition of time series can benefit forecasting done by statistical methods but not by machine learning ones. Eng. Proc. 5(1), 42 (2021).

Google Scholar

De Myttenaere, A., Golden, B., Le Grand, B. & Rossi, F. Mean absolute percentage error for regression models. Neurocomputing 192, 3848 (2016).

Article Google Scholar

Paula, M., Marilaine, C., Nuno, F. J. & Wallace, C. Predicting long-term wind speed in wind farms of northeast brazil: A comparative analysis through machine learning models. IEEE Lat. Am. Trans. 18, 20112018 (2020).

Article Google Scholar

Botchkarev, A. Evaluating performance of regression machine learning models using multiple error metrics in azure machine learning studio. Available at SSRN 3177507. (2018).

Knez, Z., Skerget, M., Sencar-Bozic, P. & Rizner, A. Solubility of nifedipine and nitrendipine in supercritical CO2. J. Chem. Eng. Data 40, 216220 (1995).

CAS Article Google Scholar

Read more:

Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning | Scientific...