scholarly journals The importance of the accuracy of the experimental data for the prediction of solubility

2010 ◽  
Vol 75 (4) ◽  
pp. 483-495 ◽  
Author(s):  
Slavica Eric ◽  
Marko Kalinic ◽  
Aleksandar Popovic ◽  
Halid Makic ◽  
Elvisa Civic ◽  
...  
Keyword(s):  
Experimental Data ◽  
Computational Models ◽  
Linear Regression Analysis ◽  
Heuristic Method ◽  
Parameter Analysis ◽  
Solubility Data ◽  
Data Sets ◽  
Data Set ◽  
Experimental Solubility Data ◽  
Experimental Solubility

Aqueous solubility is an important factor influencing several aspects of the pharmacokinetic profile of a drug. Numerous publications present different methodologies for the development of reliable computational models for the prediction of solubility from structure. The quality of such models can be significantly affected by the accuracy of the employed experimental solubility data. In this work, the importance of the accuracy of the experimental solubility data used for model training was investigated. Three data sets were used as training sets - Data Set 1 containing solubility data collected from various literature sources using a few criteria (n = 319), Data Set 2 created by substituting 28 values from Data set 1 with uniformly determined experimental data from one laboratory (n = 319) and Data Set 3 created by including 56 additional components, for which the solubility was also determined under uniform conditions in the same laboratory, in the Data Set 2 (n = 375). The selection of the most significant descriptors was performed by the heuristic method, using one-parameter and multi-parameter analysis. The correlations between the most significant descriptors and solubility were established using multi-linear regression analysis (MLR) for all three investigated data sets. Notable differences were observed between the equations corresponding to different data sets, suggesting that models updated with new experimental data need to be additionally optimized. It was successfully shown that the inclusion of uniform experimental data consistently leads to an improvement in the correlation coefficients. These findings contribute to an emerging consensus that improving the reliability of solubility prediction requires the inclusion of many diverse compounds for which solubility was measured under standardized conditions in the data set.

scholarly journals Solubility of Acetophenone in Supercritical Carbon Dioxide

2016 ◽  
Vol 10 (1) ◽  
pp. 18-28 ◽  
Author(s):  
Maria Y. Dwi ◽  
Jessica Julian ◽  
Jindrayani N. Putro ◽  
Adi T. Nugraha ◽  
Yi-Hsu Ju ◽  
...  
Keyword(s):  
Experimental Data ◽  
Carbon Dioxide ◽  
Equation Of State ◽  
Supercritical Carbon Dioxide ◽  
Static Method ◽  
Solubility Data ◽  
Experimental Solubility Data ◽  
Experimental Solubility ◽  
Robinson Equation ◽  
Supercritical Carbon

The solubility data of acetophenone in supercritical carbon dioxide (scCO2) were measured using a static method at several temperatures (313.15, 323.15, 333.15, and 343.15K) and pressures ranging from10 MPa to 28 MPa. The density based models (Chrastil and Del valle– Aguilera models) and the Peng-Robinson equation of state (PR-EOS) with quadratic and Stryjek-Vera combining rules were employed to correlate the experimental data. Good correlations between the calculated and experimental solubility data were obtained. The sum of squared errors (SSE) are 0.38 % and 0.37 % for Chrastil and Del Valle – Aguilera models, respectively; and 9.07 % for Peng-Robinson equation of state with quadratic combining rule and 4.00 % for Peng-Robinson equation of state with Stryjek-Vera combining rule.

Solubility of methane in octane + ethanol at temperatures from 303.15 K to 333.15 K and pressures up to 12.01 MPa

2012 ◽  
Vol 66 (4) ◽  
Author(s):  
Jun-Qing Cai ◽  
Jin-Zhi Qiao
Keyword(s):  
Experimental Data ◽  
Equation Of State ◽  
Solubility Data ◽  
Mixing Rules ◽  
Average Deviation ◽  
Experimental Solubility Data ◽  
Experimental Solubility

AbstractSolubility of methane in octane + ethanol was measured at temperatures ranging from 303.15 K to 333.15 K and pressures ranging from 2.60 MPa to 12.01 MPa. Experimental data were analyzed using the Soave-Redlich-Kwong equation of state with three types of mixing rules, and the estimated average deviation from the experimental solubility data was less than 3.5 %.

scholarly journals Determination of Reactivity Ratios from Binary Copolymerization Using the k-Nearest Neighbor Non-Parametric Regression

Polymers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3811
Author(s):  
Iosif Sorin Fazakas-Anca ◽  
Arina Modrea ◽  
Sorin Vlase
Keyword(s):  
Experimental Data ◽  
Nearest Neighbor ◽  
Optimization Method ◽  
Reactivity Ratios ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Integration Algorithm ◽  
Data Set ◽  
Parametric Regression ◽  
Non Parametric

This paper proposes a new method for calculating the monomer reactivity ratios for binary copolymerization based on the terminal model. The original optimization method involves a numerical integration algorithm and an optimization algorithm based on k-nearest neighbour non-parametric regression. The calculation method has been tested on simulated and experimental data sets, at low (<10%), medium (10–35%) and high conversions (>40%), yielding reactivity ratios in a good agreement with the usual methods such as intersection, Fineman–Ross, reverse Fineman–Ross, Kelen–Tüdös, extended Kelen–Tüdös and the error in variable method. The experimental data sets used in this comparative analysis are copolymerization of 2-(N-phthalimido) ethyl acrylate with 1-vinyl-2-pyrolidone for low conversion, copolymerization of isoprene with glycidyl methacrylate for medium conversion and copolymerization of N-isopropylacrylamide with N,N-dimethylacrylamide for high conversion. Also, the possibility to estimate experimental errors from a single experimental data set formed by n experimental data is shown.

scholarly journals Inferring parameters for a lattice-free model of cell migration and proliferation using experimental data

2017 ◽  
Author(s):  
Alexander P. Browning ◽  
Scott W. McCue ◽  
Rachelle N. Binny ◽  
Michael J. Plank ◽  
Esha T. Shah ◽  
...  
Keyword(s):  
Experimental Data ◽  
Cell Migration ◽  
Spatial Clustering ◽  
Cancer Cell Line ◽  
Movement Direction ◽  
Data Sets ◽  
Collective Cell Migration ◽  
Rejection Sampling ◽  
Data Set ◽  
Cell Migration And Proliferation

AbstractCollective cell spreading takes place in spatially continuous environments, yet it is often modelled using discrete lattice-based approaches. Here, we use data from a series of cell proliferation assays, with a prostate cancer cell line, to calibrate a spatially continuous individual based model (IBM) of collective cell migration and proliferation. The IBM explicitly accounts for crowding effects by modifying the rate of movement, direction of movement, and the rate of proliferation by accounting for pair-wise interactions. Taking a Bayesian approach we estimate the free parameters in the IBM using rejection sampling on three separate, independent experimental data sets. Since the posterior distributions for each experiment are similar, we perform simulations with parameters sampled from a new posterior distribution generated by combining the three data sets. To explore the predictive power of the calibrated IBM, we forecast the evolution of a fourth experimental data set. Overall, we show how to calibrate a lattice-free IBM to experimental data, and our work highlights the importance of interactions between individuals. Despite great care taken to distribute cells as uniformly as possible experimentally, we find evidence of significant spatial clustering over short distances, suggesting that standard mean-field models could be inappropriate.

scholarly journals Model Distribution Effects on Likelihood Ratios in Fire Debris Analysis

Separations ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 44 ◽  
Author(s):  
Alyssa Allen ◽  
Mary Williams ◽  
Nicholas Thurn ◽  
Michael Sigman
Keyword(s):  
Computational Models ◽  
Area Under The Curve ◽  
Ground Truth ◽  
Data Sets ◽  
Likelihood Ratios ◽  
Data Set ◽  
Discriminant Model ◽  
Fire Debris ◽  
Characteristic Area ◽  
Ignitable Liquid

Computational models for determining the strength of fire debris evidence based on likelihood ratios (LR) were developed and validated against data sets derived from different distributions of ASTM E1618-14 designated ignitable liquid class and substrate pyrolysis contributions using in-silico generated data. The models all perform well in cross validation against the distributions used to generate the model. However, a model generated based on data that does not contain representatives from all of the ASTM E1618-14 classes does not perform well in validation with data sets that contain representatives from the missing classes. A quadratic discriminant model based on a balanced data set (ignitable liquid versus substrate pyrolysis), with a uniform distribution of the ASTM E1618-14 classes, performed well (receiver operating characteristic area under the curve of 0.836) when tested against laboratory-developed casework-relevant samples of known ground truth.

scholarly journals An Interpretable Computer-Aided Diagnosis Method for Periodontitis From Panoramic Radiographs

2021 ◽  
Vol 12 ◽  
Author(s):  
Haoyang Li ◽  
Juexiao Zhou ◽  
Yi Zhou ◽  
Qiang Chen ◽  
Yangyang She ◽  
...  
Keyword(s):  
Computational Models ◽  
Alveolar Bone ◽  
Calibration Method ◽  
Disease Diagnosis ◽  
Chronic Inflammatory Disease ◽  
Data Sets ◽  
Data Set ◽  
Panoramic Radiographs ◽  
The Individual ◽  
Severity Degree

Periodontitis is a prevalent and irreversible chronic inflammatory disease both in developed and developing countries, and affects about 20–50% of the global population. The tool for automatically diagnosing periodontitis is highly demanded to screen at-risk people for periodontitis and its early detection could prevent the onset of tooth loss, especially in local communities and health care settings with limited dental professionals. In the medical field, doctors need to understand and trust the decisions made by computational models and developing interpretable models is crucial for disease diagnosis. Based on these considerations, we propose an interpretable method called Deetal-Perio to predict the severity degree of periodontitis in dental panoramic radiographs. In our method, alveolar bone loss (ABL), the clinical hallmark for periodontitis diagnosis, could be interpreted as the key feature. To calculate ABL, we also propose a method for teeth numbering and segmentation. First, Deetal-Perio segments and indexes the individual tooth via Mask R-CNN combined with a novel calibration method. Next, Deetal-Perio segments the contour of the alveolar bone and calculates a ratio for individual tooth to represent ABL. Finally, Deetal-Perio predicts the severity degree of periodontitis given the ratios of all the teeth. The Macro F1-score and accuracy of the periodontitis prediction task in our method reach 0.894 and 0.896, respectively, on Suzhou data set, and 0.820 and 0.824, respectively on Zhongshan data set. The entire architecture could not only outperform state-of-the-art methods and show robustness on two data sets in both periodontitis prediction, and teeth numbering and segmentation tasks, but also be interpretable for doctors to understand the reason why Deetal-Perio works so well.

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