scholarly journals QSPR Models for Octane Number Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Jabir H. Al-Fahemi ◽  
Nahla A. Albis ◽  
Elshafie A. M. Gad
Keyword(s):  
Octane Number ◽  
Statistical Significance ◽  
Principal Component ◽  
Coefficient Of Determination ◽  
Structure Property ◽  
Qspr Model ◽  
Data Set ◽  
Multiple Variables ◽  
Validation Set ◽  
The Relationship

Quantitative structure-property relationship (QSPR) is performed as a means to predict octane number of hydrocarbons via correlating properties to parameters calculated from molecular structure; such parameters are molecular mass M, hydration energy EH, boiling point BP, octanol/water distribution coefficient logP, molar refractivity MR, critical pressure CP, critical volume CV, and critical temperature CT. Principal component analysis (PCA) and multiple linear regression technique (MLR) were performed to examine the relationship between multiple variables of the above parameters and the octane number of hydrocarbons. The results of PCA explain the interrelationships between octane number and different variables. Correlation coefficients were calculated using M.S. Excel to examine the relationship between multiple variables of the above parameters and the octane number of hydrocarbons. The data set was split into training of 40 hydrocarbons and validation set of 25 hydrocarbons. The linear relationship between the selected descriptors and the octane number has coefficient of determination (R2=0.932), statistical significance (F=53.21), and standard errors (s =7.7). The obtained QSPR model was applied on the validation set of octane number for hydrocarbons giving RCV2=0.942 and s=6.328.

scholarly journals Children with 5′-end NF1 gene mutations are more likely to have glioma

2017 ◽  
Vol 3 (5) ◽  
pp. e192 ◽  
Author(s):  
Corina Anastasaki ◽  
Stephanie M. Morris ◽  
Feng Gao ◽  
David H. Gutmann
Keyword(s):  
Gene Mutation ◽  
Statistical Significance ◽  
Gene Mutations ◽  
Neurofibromatosis Type ◽  
Published Data ◽  
Data Sets ◽  
Nonsense Mutations ◽  
Data Set ◽  
Nf1 Gene ◽  
The Relationship

Objective:To ascertain the relationship between the germline NF1 gene mutation and glioma development in patients with neurofibromatosis type 1 (NF1).Methods:The relationship between the type and location of the germline NF1 mutation and the presence of a glioma was analyzed in 37 participants with NF1 from one institution (Washington University School of Medicine [WUSM]) with a clinical diagnosis of NF1. Odds ratios (ORs) were calculated using both unadjusted and weighted analyses of this data set in combination with 4 previously published data sets.Results:While no statistical significance was observed between the location and type of the NF1 mutation and glioma in the WUSM cohort, power calculations revealed that a sample size of 307 participants would be required to determine the predictive value of the position or type of the NF1 gene mutation. Combining our data set with 4 previously published data sets (n = 310), children with glioma were found to be more likely to harbor 5′-end gene mutations (OR = 2; p = 0.006). Moreover, while not clinically predictive due to insufficient sensitivity and specificity, this association with glioma was stronger for participants with 5′-end truncating (OR = 2.32; p = 0.005) or 5′-end nonsense (OR = 3.93; p = 0.005) mutations relative to those without glioma.Conclusions:Individuals with NF1 and glioma are more likely to harbor nonsense mutations in the 5′ end of the NF1 gene, suggesting that the NF1 mutation may be one predictive factor for glioma in this at-risk population.

Reconceptualizing Church and State: A Theoretical and Empirical Analysis of the Impact of Separation of Religion and State on Democracy

2011 ◽  
Vol 4 (2) ◽  
pp. 229-263 ◽  
Author(s):  
Robert Brathwaite ◽  
Andrew Bramsen
Keyword(s):  
Principal Component Analysis ◽  
Empirical Analysis ◽  
Church And State ◽  
Principal Component ◽  
Set Covering ◽  
Data Set ◽  
Religion And State ◽  
The Impact ◽  
The Relationship ◽  
State Separation

AbstractThis article argues that the relationship between democracy and the separation of religion and state needs to be reexamined. We argue that previous studies have misconceptualized the impact that a lack of church-state separation can have on democracy, or have taken a narrow focus by concentrating on specific cases. We use principal component analysis and a large-ndata set covering 125 countries to show that the separation of religion and state should be conceptualized multi-dimensionally and that it should be considered a component of democracy. Our findings show that as separation of religion and state increases, the level of democracy also increases.

QSPR Study on Electric Spark Sensitivity of Nitro Arenes

2011 ◽  
Vol 284-286 ◽  
pp. 197-200 ◽  
Author(s):  
Rui Wang ◽  
Jun Cheng Jiang ◽  
Yong Pan
Keyword(s):  
Function Approximation ◽  
Significant Contribution ◽  
Correlation Coefficients ◽  
Structure Property ◽  
Qspr Model ◽  
Structure Property Relationship ◽  
Leave One Out ◽  
The Relationship ◽  
Prediction Capability ◽  
Good Agreement

A quantitative structure-property relationship (QSPR) model was proposed for predicting electric spark sensitivity of 39 nitro arenes. The genetic function approximation (GFA) was employed to select the descriptors that have significant contribution to electric spark sensitivity from various descriptors and for fitting the relationship existed between the selected 8 descriptors and electric spark sensitivity. The correlation coefficients (R2) together with correlation coefficient of the leave-one-out cross validation (Q2CV) of the model are 0.924 and 0.873, respectively. The model is highly statistically significant, and the robustness as well as internal prediction capability of which is satisfactory. The results show that the predicted electric spark sensitivity values are in good agreement with the experimental data.

scholarly journals DATA MINING FOR PREDICTING THE AMOUNT OF COFFEE PRODUCTION USING CRISP-DM METHOD

2020 ◽  
Vol 17 (1) ◽  
pp. 1-8
Author(s):  
Ali Khumaidi
Keyword(s):  
Coefficient Of Determination ◽  
Data Preparation ◽  
Coffee Production ◽  
Data Set ◽  
Coffee Plantations ◽  
Preparation Stage ◽  
Coffee Plants ◽  
Internal And External Factors ◽  
The Relationship

The production of coffee plantations has become the leading plantation commodity with the export value of the fourth rank after oil palm, rubber and coconut. The number of coffee needs for export every year always increases, therefore it is necessary to predict the yield of coffee plants to estimate planting and anticipation that will be done so as to achieve the target. Coffee plant productivity is influenced by internal and external factors, namely the quality of the plant itself, soil, altitude and climate. The method used in this study is the CRISP-DM method and multiple linear regression algorithm to predict the amount of coffee production and determine the relationship between the variables. The steps taken are business understanding, data understanding, data preparation, modeling and evaluation. The data set that is used as many as 170 data after going through the data preparation stage produced 150 data with 5 attributes in the table. With calculations using tools, the coefficient of determination is 91.96%. That the variation in the value of the production of coffee plants is influenced by independent variables, namely the area of ​​plantations, rainfall, air pressure and solar radiation by 91.96% and 8.04% influenced by other variables not measured in this study. The results of the evaluation and validation of predictions produce good accuracy with an RMSE value of 0.3477.

DIGITAL COMPETITIVENESS OF UKRAINE: GENERAL TRENDS AND IMPACT MODELING

2018 ◽  
Author(s):  
Nadiya Yavorska ◽  
◽  
Tetyana Danko ◽  
Keyword(s):  
Inverse Relationship ◽  
New Technologies ◽  
Statistical Significance ◽  
Regression Equation ◽  
Coefficient Of Determination ◽  
Multiple Regression Equation ◽  
Individual Factors ◽  
Decisive Influence ◽  
The Impact ◽  
The Relationship

The object of the study is the digital competitiveness of the country and its impact on GDP. The paper summarizes the methodology for determining the rating of global digital competitiveness and investigates the impact of digital competitiveness on GDP using econometric analysis methods. The methodological basis of the study was the fundamental principles of economic theory, statistics and econometrics. To develop a statistical model of the relationship between digital competitiveness and GDP, correlation analysis was performed using the pairwise regression equation, and to influence individual factors - a linear multiple regression equation. The parameters of the constructed models by the method of least squares are estimated and their statistical significance is checked. The results of the study show that there is a close inverse relationship between the rating on the Digital Competitiveness Index and GDP. This is due to the fact that the linear correlation coefficient is -0.819, and the value of the coefficient of determination (0.6712) shows the decisive influence of digital competitiveness on GDP. Verification of the statistical significance of the constructed model allowed to recognize it as statistically reliable, which allows to use it for forecasting. Instead, the resulting econometric model of the relationship between individual factors of digital competitiveness rating and GDP is characterized by a strong inverse relationship between the two factors "Knowledge" and "Technology" and a direct relationship between the factor "Readiness for the future". The factor of "Knowledge", which characterizes the process of digital transformation of Ukraine through understanding, studying and creating new technologies, has a decisive influence on the volume of GDP. The developed model of the relationship between individual factors of digital competitiveness rating and GDP, as adequate and statistically significant, can be used for further analysis and forecasting. It is proved that the process of digitalization is an urgent need for the existence of the economic system at present, namely the introduction of digital technologies can increase the competitiveness of the country on the world stage.

Exploring students digital activities and performances through their activities logged in learning management system using educational data mining approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Adel Bessadok ◽  
Ehab Abouzinadah ◽  
Osama Rabie
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Management System ◽  
Learning Management System ◽  
Statistical Significance ◽  
Educational Data Mining ◽  
Data Set ◽  
Content Type ◽  
Learning Management ◽  
The Relationship

Purpose This paper aims to investigate the relationship between the students’ digital activities and their academic performance through two stages. In the first stage, students’ digital activities were studied and clustered based on the attributes of their activity log of learning management system (LMS) data set. In the second stage, the significance of the relationship between these profiles and the associated academic performance was tested statistically. Design/methodology/approach The LMS delivers E-learning courses and keeps track of the students’ activities. Investigating these students’ digital activities became a real challenge. The diversity of students’ involvement in the learning process was proven through the LMS which characterize students’ specific profiles. The Educational Data Mining (EDM) approach was used to discover students’ learning profiles and associated academic performances, where the activity log file exemplified their activities hosted in the LMS. The sample study data is from an undergraduate e-course hosted on the platform of Blackboard LMS offered at a Saudi University during the first semester of the 2019–2020 academic year. The chosen undergraduate course had 25 sections, and the students attending came from science, technology, engineering and math background. Findings Results show three clusters based on the digital activities of the students. The correlation test shows the statistical significance and proves the effect of the student’s profile on his academic performance. The data analysis shows that students with different profiles can still get similar academic performance using LMS. Originality/value This empirical study emphasizes the importance of the EDM approach using clustering techniques which can help the instructor understand how students use the provided LMS content to learn and then can deliver them the best educational experience.

scholarly journals Clipping aneurysms improves outcomes for patients undergoing coiling

2019 ◽  
Vol 130 (5) ◽  
pp. 1491-1497 ◽  
Author(s):  
Ian A. Anderson ◽  
Ahilan Kailaya-Vasan ◽  
Richard J. Nelson ◽  
Christos M. Tolias
Keyword(s):  
Endovascular Procedures ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Statistical Significance ◽  
Mortality Rates ◽  
Endovascular Techniques ◽  
Data Set ◽  
Case Selection ◽  
The One ◽  
The Relationship ◽  
One Way Anova

OBJECTIVEMost intracranial aneurysms are now treated by endovascular rather than by microsurgical procedures. There is evidence to demonstrate superior outcomes for patients with aneurysmal subarachnoid hemorrhage (aSAH) treated by endovascular techniques. However, some cases continue to require microsurgery. The authors have examined the relationship between the number of aneurysms treated by microsurgery and outcome for patients undergoing treatment for aSAH at neurosurgical centers in England.METHODSThe Neurosurgical National Audit Programme (NNAP) database was used to identify aSAH cases and to provide associated 30-day mortality rates for each of the 24 neurosurgical centers in England. Data were compared for association by regression analysis using the Pearson product-moment correlation coefficient and any associations were tested for statistical significance using the one-way ANOVA test. The NNAP data were validated utilizing a second, independent registry: the British Neurovascular Group’s (BNVG) National Subarachnoid Haemorrhage Database.RESULTSIncreasing numbers of microsurgical cases in a center are associated with lower 30-day mortality rates for all patients treated for aSAH, irrespective of treatment modality (Pearson r = 0.42, p = 0.04), and for patients treated for aSAH by endovascular procedures (Pearson r = 0.42, p = 0.04). The correlations are stronger if all (elective and acute) microsurgical cases are compared with outcome. The BNVG data validated the NNAP data set for patients with aSAH.CONCLUSIONSThere is a statistically significant association between local microsurgical activity and center outcomes for patients with aSAH, even for patients treated endovascularly. The authors postulate that the number of microsurgical cases performed may be a surrogate indicator of closer neurosurgical involvement in the overall management of neurovascular patients and of optimal case selection.

Factor Analysis, Random Data, and Patterned Results

1981 ◽  
Vol 46 (2) ◽  
pp. 272-283 ◽  
Author(s):  
Robert K. Vierra ◽  
David L. Carlson
Keyword(s):  
Factor Analysis ◽  
Correlation Matrix ◽  
Statistical Significance ◽  
Principal Component ◽  
Multivariate Statistical Techniques ◽  
Random Data ◽  
Multivariate Statistical ◽  
Data Set ◽  
Bartlett’S Test ◽  
The Matrix

Multivariate statistical techniques such as factor analysis are capable of producing patterned results with most, if not all, data matrices. This paper demonstrates that patterned results are obtainable when principal component analysis is applied to a random data set. It is suggested that Bartlett's test for the statistical significance of a correlation matrix be employed in deciding whether a factor analysis of the matrix is justified.

Tumor mutational burden (TMB) and response rates to immune checkpoint inhibitors (ICIs) targeting PD-1, CTLA-4, and combination.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 2578-2578 ◽  
Author(s):  
Arsen Osipov ◽  
Aleksandra Popovic ◽  
Alexander Hopkins ◽  
Garrett M. Frampton ◽  
Lee A. Albacker ◽  
...  
Keyword(s):  
Checkpoint Inhibitors ◽  
Statistical Significance ◽  
Objective Response ◽  
Strong Relationship ◽  
Coefficient Of Determination ◽  
Clinical Activity ◽  
Average Improvement ◽  
Tumor Mutational Burden ◽  
Tumor Types ◽  
The Relationship

2578 Background: ICIs targeting PD-1/L1 and/or CTLA-4 have activity against many different cancers. We and others have previously shown that a higher TMB, a surrogate for an increased number of expressed tumor neoantigens, is an important biomarker for response to anti-PD-1/L1 monotherapy. Whether the relationship between the TMB and response to ICIs extends beyond anti-PD-1/L1 is unknown. Methods: We identified 30 major solid tumor types for which TMB has been described using a genomic profiling assay performed by Foundation Medicine. We conducted searches of MEDLINE (from Jan 1, 2010 to Jan 20, 2019), as well as abstracts presented at ASCO, ESMO, AACR Annual Meetings 2010-2018 to identify the objective response rate (ORR) for anti-PD-1/L1, anti-CTLA-4 and combination anti-PD-1/L1 plus anti-CTLA-4, in each of these cancer types. We pooled the response data from the largest published studies that evaluated the ORR. We excluded studies that; enrolled < 10 evaluable patients, investigated ICI therapies in combination with other agents, and studies that selected patients based on immune-related biomarkers. Across tumor types, median TMB was compared to ORR utilizing the coefficient of determination (r2) derived from simple linear regressions. Results: TMB is strongly associated with response to anti-PD-1/L1 monotherapy (n = 8798, r2= 0.4704, p < 0.001), and combination anti-PD-1/L1 plus anti-CTLA-4 (n = 2280,r2= 0.4082, p = 0.004). Available ORR data were more limited with CTLA-4 monotherapy and the relationship between ORR and TMB did not meet statistical significance (n = 1377, r2= 0.2606, p = 0.1086). The additional ORR benefit of adding a CTLA-4 inhibitor to anti-PD-1/PDL1 therapy increased with increasing TMB. In tumor types with a lower TMB ( < 10 mutations/MB), combined ICI therapy led to an average improvement of 5.5% in ORR over PD-1/L1 monotherapy, versus 21.8 % ORR improvement in high TMB tumors (≥10 mutations/MB). Conclusions: A strong relationship exists between TMB and clinical activity of both PD-1/L1 monotherapy and combination ICIs with PD-1/L1 plus CTLA-4. The clinical benefit of adding anti-CTLA-4 to anti-PD-1/L1 is greatest in high TMB tumors and limited in low TMB tumors.

scholarly journals A simple 2D-QSPR model for the prediction of Setschenow constants of organic compounds

2016 ◽  
Vol 35 (1) ◽  
pp. 53 ◽  
Author(s):  
Qi Xu ◽  
Lingling Fan ◽  
Jie Xu
Keyword(s):  
Organic Compounds ◽  
External Validation ◽  
Structure Property ◽  
Training Set ◽  
Multilinear Regression ◽  
Qspr Model ◽  
Multilinear Regression Analysis ◽  
Validation Set ◽  
Reliability And Robustness ◽  
Leave One Out

A quantitative structure-property relationship (QSPR) analysis of the Setschenow constants (Ksalt) of organic compounds in a sodium chloride solution was carried out using only two-dimensional (2D) descriptors as input parameters. The whole set of 101 compounds was split into a training set of 71 compounds and a validation set of 30 compounds by means of the Kennard and Stones algorithm. A general four-parameter equation, with correlation coefficient (R) of 0.887 and standard error of estimation (s) of 0.031, was obtained by stepwise multilinear regression analysis (MLRA) on the training set. The reliability and robustness of the present model was verified with leave-one-out cross-validation, randomization tests, and the external validation set. All of the descriptors contained in this model are calculated directly from the molecular 2D structures; thus, this model can be used to easily predict the Ksalt of other compounds not involved in the present dataset.

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