Solubility enhancement of pantoprazole sodium sesquihydrate through supercritical solvent: machine learning based study

[1]  S. Sarkar,et al.  Scope of machine learning applications for addressing the challenges in next-generation wireless networks , 2022, CAAI Trans. Intell. Technol..

[2]  Chandrasekhar Garlapati,et al.  Solubility measurement and thermodynamic modeling of pantoprazole sodium sesquihydrate in supercritical carbon dioxide , 2022, Scientific Reports.

[3]  Huanan Bao,et al.  Rethinking the image feature biases exhibited by deep convolutional neural network models in image recognition , 2022, CAAI Trans. Intell. Technol..

[4]  Faiyaz Ahmad Deep image retrieval using artificial neural network interpolation and indexing based on similarity measurement , 2022, CAAI Trans. Intell. Technol..

[5]  Farbod Farmand,et al.  Pantoprazole-Associated Thrombocytopenia: A Literature Review and Case Report , 2022, Cureus.

[6]  Z. Nasrollahi,et al.  Enhancement of supercritical carbon dioxide solubility models using molecular simulation data , 2022, Korean Journal of Chemical Engineering.

[7]  M. Alashwal,et al.  Prediction of busulfan solubility in supercritical CO2 using tree-based and neural network-based methods , 2022, Journal of Molecular Liquids.

[8]  Jeong-Sook Park,et al.  Application of supercritical fluid technology for solid dispersion to enhance solubility and bioavailability of poorly water-soluble drugs. , 2021, International journal of pharmaceutics.

[9]  Suzanne Lacasse,et al.  State-of-the-art review of soft computing applications in underground excavations , 2020, Geoscience Frontiers.

[10]  Leandro dos Santos Coelho,et al.  Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series , 2020, Appl. Soft Comput..

[11]  Andrew C. Michaels Artificial Intelligence, Legal Change, and Separation of Powers , 2019 .

[12]  Alexei Botchkarev,et al.  Evaluating Performance of Regression Machine Learning Models Using Multiple Error Metrics in Azure Machine Learning Studio , 2018 .

[13]  John B. O. Mitchell,et al.  Can human experts predict solubility better than computers? , 2017, Journal of Cheminformatics.

[14]  Lazaros G. Papageorgiou,et al.  A regression tree approach using mathematical programming , 2017, Expert Syst. Appl..

[15]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[16]  B. Ripley Classification and Regression Trees , 2015 .

[17]  Özgür Kisi,et al.  Modeling rainfall-runoff process using soft computing techniques , 2013, Comput. Geosci..

[18]  Neeraj Bhargava,et al.  Decision Tree Analysis on J48 Algorithm for Data Mining , 2013 .

[19]  R. Saudagar,et al.  ON SOLUBILITY ENHANCEMENT TECHNIQUES , 2013 .

[20]  Jignasa K. Savjani,et al.  Drug Solubility: Importance and Enhancement Techniques , 2012, ISRN pharmaceutics.

[21]  Y. Krishnaiah Pharmaceutical Technologies for Enhancing Oral Bioavailability of Poorly Soluble Drugs , 2010 .

[22]  A. Behera,et al.  Enhancement of Solubility: A Pharmaceutical Overview , 2010 .

[23]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[24]  C. Calabrese,et al.  Long-term management of GERD in the elderly with pantoprazole , 2007, Clinical interventions in aging.

[25]  Leo Breiman,et al.  Using Iterated Bagging to Debias Regressions , 2001, Machine Learning.

[26]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[27]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[28]  F. Dehghani,et al.  Dense gas anti-solvent processes for pharmaceutical formulation , 2003 .

[29]  D. Faulds,et al.  Pantoprazole , 2012, Drugs.

[30]  Robert E. Schapire,et al.  The Boosting Approach to Machine Learning An Overview , 2003 .

[31]  Agostino Di Ciaccio,et al.  Improving nonparametric regression methods by bagging and boosting , 2002 .

[32]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[33]  P. Jungnickel Pantoprazole: a new proton pump inhibitor. , 2000, Clinical therapeutics.

[34]  David W. Opitz,et al.  An Empirical Evaluation of Bagging and Boosting , 1997, AAAI/IAAI.

[35]  G. Brunner,et al.  Solubilities of the Fat-Soluble Vitamins A, D, E, and K in Supercritical Carbon Dioxide , 1997 .

[36]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[37]  J. Ross Quinlan,et al.  Learning decision tree classifiers , 1996, CSUR.

[38]  Fred L. Collopy,et al.  Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .

[39]  N. Nagelkerke,et al.  A note on a general definition of the coefficient of determination , 1991 .

[40]  David A. Landgrebe,et al.  A survey of decision tree classifier methodology , 1991, IEEE Trans. Syst. Man Cybern..

[41]  M R Segal,et al.  A comparison of estimated proportional hazards models and regression trees. , 1989, Statistics in medicine.