scholarly journals THEORETICAL AND INSILCO PHARMACOKINETIC INVESTIGATIONS ON SOME PHENYL PIPERIDINE DERIVATIVES AS NOVEL ANTIDEPRESSANT AGENTS

2020 ◽  
Vol 6 (2) ◽  
pp. 0107-0128
Author(s):  
Olasupo Sabitu Babatunde Babatunde ◽  
Adamu Uzairu ◽  
Gideon Adamu Shallangwa ◽  
Sani Uba
Keyword(s):  
Regression Models ◽  
Density Functional ◽  
Mean Squared Error ◽  
Linear Regression Models ◽  
Statistical Parameters ◽  
Functional Theory ◽  
Squared Error ◽  
Antipsychotic Activity ◽  
Chi Squared ◽  
Multi Linear Regression

A theoretical bioinformatic investigation was carried out on some Inhibitors of serotonin transporter (SERT) of Phenyl piperidine derivatives using Density Functional Theory (DFT/B3LYP/6-31G*) at ground state with Spartan 14 V1.1.4 software in modeling the antipsychotic activity of the compounds. The molecular descriptors were computed using the PaDEL-Descriptor software 2.20 version. Penta-parametric Multi-linear regression models were developed using the MLR-GFA for selecting the most important descriptors. The statistical parameters for the best model are; R2Train= 0.8572, R 2adj = 0.8274, R2Test = 0.678, Q2cv (LOO) = 0.7664, Ꭓ2= 0.0036, r2m (LOO)= 0.694 and Delta r2m (LOO)= 0.0051). Also, the estimated Chi-squared (Ꭓ2= 0.0036), Root-mean squared error (RMSE= 0.168) and and

Mean-squared error experiment design for linear regression models*

2012 ◽  
Vol 45 (16) ◽  
pp. 1629-1634 ◽  
Author(s):  
Diego Eckhard ◽  
Håkan Hjalmarsson ◽  
Cristian R. Rojas ◽  
Michel Gevers
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Mean Squared Error ◽  
Experiment Design ◽  
Linear Regression Models ◽  
Squared Error

scholarly journals Predicting the band gap of binary compounds from machine-learning regression methods

2021 ◽  
Author(s):  
Mengbo Guo ◽  
Xuyang Xu ◽  
Han Xie
Keyword(s):  
Machine Learning ◽  
Band Gap ◽  
Density Functional ◽  
Mean Squared Error ◽  
Gradient Boosting ◽  
Functional Theory ◽  
Regression Methods ◽  
Squared Error ◽  
State Band ◽  
Binary Compounds

Density functional theory (DFT) is a ubiquitous first-principles method, but the approximate nature of the exchange-correlation functional poses an inherent limitation for the accuracy of various computed properties. In this context, surrogate models based on machine learning have the potential to provide a more efficient and physically meaningful understanding of electronic properties, such as the band gap. Here, we construct a gradient boosting regression (GBR) model for prediction of the band gap of binary compounds from simple physical descriptors, using a dataset of over 4000 DFT-computed band gaps. Out of 27 features, electronegativity, periodic group, and highest occupied energy level exhibit the highest importance score, consistent with the underlying physics of the electronic structure. We obtain a model accuracy of 0.81 and root mean squared error of 0.26 eV using the top five features, achieving accuracy comparable to previously reported values but employing less number of features. Our work presents a rapid and interpretable prediction model for solid-state band gap with high fidelity to DFT and can be extended beyond binary materials considered in this study.

scholarly journals Analysis of SF-6D Health State Utility Scores: Is Beta Regression Appropriate?

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 525
Author(s):  
Samer A Kharroubi
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Random Effects ◽  
Regression Models ◽  
Mean Squared Error ◽  
Health State Utility ◽  
Beta Regression ◽  
Health State ◽  
Linear Regression Models ◽  
Utility Scores

Background: Typically, modeling of health-related quality of life data is often troublesome since its distribution is positively or negatively skewed, spikes at zero or one, bounded and heteroscedasticity. Objectives: In the present paper, we aim to investigate whether Bayesian beta regression is appropriate for analyzing the SF-6D health state utility scores and respondent characteristics. Methods: A sample of 126 Lebanese members from the American University of Beirut valued 49 health states defined by the SF-6D using the standard gamble technique. Three different models were fitted for SF-6D via Bayesian Markov chain Monte Carlo (MCMC) simulation methods. These comprised a beta regression, random effects and random effects with covariates. Results from applying the three Bayesian beta regression models were reported and compared based on their predictive ability to previously used linear regression models, using mean prediction error (MPE), root mean squared error (RMSE) and deviance information criterion (DIC). Results: For the three different approaches, the beta regression model was found to perform better than the normal regression model under all criteria used. The beta regression with random effects model performs best, with MPE (0.084), RMSE (0.058) and DIC (−1621). Compared to the traditionally linear regression model, the beta regression provided better predictions of observed values in the entire learning sample and in an out-of-sample validation. Conclusions: Beta regression provides a flexible approach to modeling health state values. It also accounted for the boundedness and heteroscedasticity of the SF-6D index scores. Further research is encouraged.

scholarly journals Regression Models for Symbolic Interval-Valued Variables

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 429
Author(s):  
Jose Emmanuel Chacón ◽  
Oldemar Rodríguez
Keyword(s):  
Regression Models ◽  
Mean Squared Error ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
R Language ◽  
Squared Error ◽  
Vector Machines ◽  
Synthetic Datasets ◽  
Interval Valued

This paper presents new approaches to fit regression models for symbolic internal-valued variables, which are shown to improve and extend the center method suggested by Billard and Diday and the center and range method proposed by Lima-Neto, E.A.and De Carvalho, F.A.T. Like the previously mentioned methods, the proposed regression models consider the midpoints and half of the length of the intervals as additional variables. We considered various methods to fit the regression models, including tree-based models, K-nearest neighbors, support vector machines, and neural networks. The approaches proposed in this paper were applied to a real dataset and to synthetic datasets generated with linear and nonlinear relations. For an evaluation of the methods, the root-mean-squared error and the correlation coefficient were used. The methods presented herein are available in the the RSDA package written in the R language, which can be installed from CRAN.

scholarly journals Combining Clustering and Regression Models for Recommending Researchers

2021 ◽  
Author(s):  
Jaimel de Oliveira Lima ◽  
Elias Oliveira
Keyword(s):  
Regression Models ◽  
Mean Squared Error ◽  
Scientific Production ◽  
Clustering Techniques ◽  
Adaptive Process ◽  
Research Areas ◽  
A Value ◽  
Squared Error ◽  
Human Effort ◽  
Ranking Information

Due to the increase in scientific production, especially in recent years, management and decision support challenge also increase significantly. The task of recommending researchers, for example, to a project is not simple. Even with the proper amount of data, ranking and recommending researchers becomes a challenging process. Despite the different methods, what can happen is that the datasets of an institution or research areas do not have a ranking value, that is, a value that can be used to assess the position of a researcher. Even having a necessary dataset, there is no ranking information for these researchers, and this process of obtaining data for training a model can be costly. We propose to use clustering techniques to support the ranking process, reducing the human effort to obtain examples for models training. Then, we used this dataset to train the regression models and Mean Squared Error (MSE) and Normalized Discounted Cumulative Gain (nDCG) to evaluate them. Tests demonstrate that our solution can support the researchers' recommendation process in an adaptive process to the needs of an organization.

scholarly journals A Statistically Supported Antioxidant Activity DFT Benchmark—The Effects of Hartree–Fock Exchange and Basis Set Selection on Accuracy and Resources Uptake

Molecules ◽  
2021 ◽  
Vol 26 (16) ◽  
pp. 5058
Author(s):  
Maciej Spiegel ◽  
Andrzej Gamian ◽  
Zbigniew Sroka
Keyword(s):  
Density Functional ◽  
Optimal Level ◽  
Basis Set ◽  
Basis Sets ◽  
Linear Regression Models ◽  
Functional Theory ◽  
Hartree Fock ◽  
Diffuse Function ◽  
Vertical Electron Affinity ◽  
High Level

Polyphenolic compounds are now widely studied using computational chemistry approaches, the most popular of which is Density Functional Theory. To ease this process, it is critical to identify the optimal level of theory in terms of both accuracy and resource usage—a challenge we tackle in this study. Eleven DFT functionals with varied Hartree–Fock exchange values, both global and range-separated hybrids, were combined with 14 differently augmented basis sets to calculate the reactivity indices of caffeic acid, a phenolic acid representative, and compare them to experimental data or a high-level of theory outcome. Aside from the main course, a validation of the widely used Janak’s theorem in the establishment of vertical ionization potential and vertical electron affinity was evaluated. To investigate what influences the values of the properties under consideration, linear regression models were developed and thoroughly discussed. The results were utilized to compute the scores, which let us determine the best and worst combinations and make broad suggestions on the final option. The study demonstrates that M06–2X/6–311G(d,p) is the best fit for such research, and, curiously, it is not necessarily essential to include a diffuse function to produce satisfactory results.

Influence of Domain and Model Properties on the Reliability Estimates' Performance

2009 ◽  
Vol 5 (4) ◽  
pp. 58-76
Author(s):  
Zoran Bosnic ◽  
Igor Kononenko
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Mean Squared Error ◽  
Noisy Data ◽  
Data Set ◽  
Squared Error ◽  
Reliability Estimates ◽  
Risk Sensitive ◽  
Average Accuracy ◽  
Applications Of Machine Learning

In machine learning, the reliability estimates for individual predictions provide more information about individual prediction error than the average accuracy of predictive model (e.g. relative mean squared error). Such reliability estimates may represent decisive information in the risk-sensitive applications of machine learning (e.g. medicine, engineering, and business), where they enable the users to distinguish between more and less reliable predictions. In the authors’ previous work they proposed eight reliability estimates for individual examples in regression and evaluated their performance. The results showed that the performance of each estimate strongly varies depending on the domain and regression model properties. In this paper they empirically analyze the dependence of reliability estimates’ performance on the data set and model properties. They present the results which show that the reliability estimates perform better when used with more accurate regression models, in domains with greater number of examples and in domains with less noisy data.

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