scholarly journals Infrared ship signature prediction, model validation, and sky radiance

2005 ◽  
Author(s):  
Filip Neele
Keyword(s):  
Prediction Model ◽  
Model Validation ◽  
Sky Radiance

scholarly journals Satellite-Based NO2 and Model Validation in a National Prediction Model Based on Universal Kriging and Land-Use Regression

2016 ◽  
Vol 50 (7) ◽  
pp. 3686-3694 ◽  
Author(s):  
Michael T. Young ◽  
Matthew J. Bechle ◽  
Paul D. Sampson ◽  
Adam A. Szpiro ◽  
Julian D. Marshall ◽  
...  
Keyword(s):  
Land Use ◽  
Prediction Model ◽  
Model Validation ◽  
Universal Kriging ◽  
Land Use Regression ◽  
Model Based

scholarly journals Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures

2022 ◽  
Author(s):  
Kousuke Nishikiori ◽  
Kentaro Tanaka ◽  
Yoshihiro Uesawa
Keyword(s):  
Prediction Model ◽  
Model Validation ◽  
Prediction Models ◽  
Removal Rate ◽  
Structural Characteristics ◽  
Estimation Method ◽  
Quantitative Structure Activity Relationship ◽  
Pharmacokinetic Parameters ◽  
Qsar Analysis ◽  
Chemical Structures

Abstract In designing drug dosing for hemodialysis patients, the removal rate (RR) of the drug by hemodialysis is important. However, acquiring the RR is difficult, and there is a need for an estimation method that can be used in clinical settings. In this study, the RR predictive model was constructed using the RR of known drugs by quantitative structure–activity relationship (QSAR) analysis. Drugs were divided into a model construction drug set (75%) and a model validation drug set (25%). The RR was collected from 143 medicines. The objective variable (RR) and chemical structural characteristics (descriptors) of the drug (explanatory variable) were used to construct a prediction model using partial least squares (PLS) regression and artificial neural network (ANN) analyses. The determination coefficients in the PLS and ANN methods were 0.586 and 0.721 for the model validation drug set, respectively. QSAR analysis successfully constructed dialysis RR prediction models that were comparable or superior to those using pharmacokinetic parameters. Considering that the RR dataset contains potential errors, we believe that this study has achieved the most reliable RR prediction accuracy currently available. These predictive RR models can be achieved using only the chemical structure of the drug. This model is expected to be applied at the time of hemodialysis. Graphic Abstract

Identification of Lysine Carboxylation Sites in Proteins by Integrating Statistical Moments and Position Relative Features via General PseAAC

2020 ◽  
Vol 15 (5) ◽  
pp. 396-407 ◽  
Author(s):  
Saba Amanat ◽  
Adeel Ashraf ◽  
Waqar Hussain ◽  
Nouman Rasool ◽  
Yaser D. Khan
Keyword(s):  
Prediction Model ◽  
Model Validation ◽  
Cross Validation ◽  
Covalent Attachment ◽  
Side Chain ◽  
Consistency Test ◽  
Post Translational Modifications ◽  
Independent Dataset ◽  
Proposed Model ◽  
Self Consistency

Background: Carboxylation is one of the most biologically important post-translational modifications and occurs on lysine, arginine, and glutamine residues of a protein. Among all these three, the covalent attachment of the carboxyl group with the lysine side chain is the most frequent and biologically important type of carboxylation. For studying such biological functions, it is essential to correctly determine the lysine sites sensitive to carboxylation. Objective: Herein, we present a computational model for the prediction of the carboxylysine site which is based on machine learning. Methods: Various position and composition relative features have been incorporated into the Pse- AAC for construction of feature vectors and a neural network is employed as a classifier. The model is validated by jackknife, cross-validation, self-consistency, and independent testing. Results: The results of the self-consistency test elaborated that model has 99.76% Acc, 99.76% Sp, 99.76% Sp, and 0.99 MCC..Using the jackknife method, prediction model validation gave 97.07% Acc, while for 10-fold cross-validation, prediction model validation gave 95.16% Acc. Conclusion: The results of independent dataset testing were 94.3% which illustrated that the proposed model has better performance as compared to the existing model PreLysCar; however, the accuracy can be improved further, in the future, due to the increasing number of carboxylysine sites in proteins.

Full-Scale Pipe Strain Test Quality and Safety Factor Determination for Strain-Based Engineering Critical Assessment

2016 ◽  
Author(s):  
Douglas P. Fairchild ◽  
Justin M. Crapps ◽  
Wentao Cheng ◽  
Huang Tang ◽  
Svetlana Shafrova
Keyword(s):  
Prediction Model ◽  
Data Base ◽  
Model Validation ◽  
Safety Factor ◽  
Accuracy Assessment ◽  
Critical Assessment ◽  
Full Scale ◽  
Lessons Learned ◽  
Test Quality ◽  
New Model

Generating a tensile strain capacity (TSC) prediction model is a difficult challenge in applied mechanics. Because current models are relatively new and extensive strain-based design (SBD) pipeline service experience does not exist, rigorous model validation using full-scale tests (FSTs) is paramount. The lessons learned from 159 FSTs were presented previously and the data base has grown to 173 tests. This data base is used to assess the accuracy of a relatively new TSC prediction model. The new model simulates a single, surface breaking weld flaw; however, some of the FSTs contained interacting or embedded flaws or unintentional weld defects, while others failed by brittle fracture, and still others experienced welding problems rendering them unsuitable for model validation. Of 173 tests, a smaller number (122, 101, or 89 depending on the goal) is used for comparison to the new model. This paper describes (1) the importance of reliable FSTs, (2) how the 173 tests were judged for suitability in model accuracy assessment, and (3) the use of the FST data to develop a safety factor for strain-based engineering critical assessment (SBECA). The safety factor is generated from a 95% upper confidence limit on the ratio of predicted-to-measured TSC. The safety factor is 1.88. Using the new model and this safety factor, a TSC prediction equation is provided for use in SBECA. The practical meaning of this is that if either TSC or tolerable defect size is calculated using the new model, then the probability of being non-conservative is estimated to be 5%.

scholarly journals Prediction Model Validation: Normal Human Pigmentation Variation

2011 ◽  
Vol 02 (08) ◽  
Author(s):  
Robert K. Valenzuela ◽  
Shosuke Ito ◽  
Kazumasa Wakamatsu
Keyword(s):  
Prediction Model ◽  
Model Validation ◽  
Normal Human

scholarly journals The importance of prediction model validation and assessment in obesity and nutrition research

2015 ◽  
Vol 40 (6) ◽  
pp. 887-894 ◽  
Author(s):  
A E Ivanescu ◽  
P Li ◽  
B George ◽  
A W Brown ◽  
S W Keith ◽  
...  
Keyword(s):  
Prediction Model ◽  
Model Validation ◽  
Nutrition Research

Phase one of a musculoskeletal injury prediction model validation: A prospective study in Navy cadets

2017 ◽  
Vol 20 ◽  
pp. S48-S49
Author(s):  
Thiago Lopes ◽  
Milena Simic ◽  
Bruno Terra ◽  
Priscila Bunn ◽  
Daniel Alves ◽  
...  
Keyword(s):  
Prediction Model ◽  
Prospective Study ◽  
Model Validation ◽  
Musculoskeletal Injury ◽  
Injury Prediction ◽  
A Prospective Study
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