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Accuracy of Single and Multi-Trait Genomic Prediction Models for Grain Yield in US Pacific Northwest Winter Wheat

2019 Crop Breeding, Genetics and Genomics  
Relatedness between populations, heritability of the secondary traits, and the type of PLS model used were among the principal factors affecting prediction ability.  ...  The prediction ability of different single and multiple trait partial least square (PLS) regression models for grain yield were assessed for winter wheat lines evaluated in US Pacific Northwest environments  ...  Jayfred Godoy, Gary Shelton, Kyall Hagemeyer, and Jason Wigen for collection of phenotypic data.  ... 
doi:10.20900/cbgg20190012 fatcat:lkuqludi4rcjvmbmtlrq34ybru

Adaptive Reweighted Minimum Vector Variance Estimator of Covariance Used for as a New Robust Approach to Partial Least Squares Regression

Esra POLAT, Hazlina ALİ
to data and prediction capability. • The proposed robust PLS-ARWMVV is robust, efficient and fitting to data set well.  ...  Highlights • A new robust PLSR method: PLS-ARWMVV is introduced. • PLS-ARWMVV is compared with ordinary PLSR and four popular robust PLSR methods. • The methods are compared in terms of efficiency, fitting  ...  If the proportion of orthogonal outliers is 0.1, RSIMPLS, PLS-SD and PLS-KurSD methods come to forefront especially in terms of efficiency and prediction ability.  ... 
doi:10.35378/gujs.642935 fatcat:26fpeykwsvdphmjjv2lqmdomli

Linear Inferential Modeling: Theoretical Perspectives, Extensions, and Comparative Analysis

Muddu Madakyaru, Mohamed N. Nounou, Hazem N. Nounou
2012 Intelligent Control and Automation  
analysis (RCCA) method, and finally to compare the performances of these techniques and highlight some of the practical issues that can affect their predictive abilities.  ...  effect of the noise on the model prediction.  ...  This is not the case in full rank models (OLS and RR) where all inputs are used to predict the model output. The results also show that the performances of PCR and PLS are comparable.  ... 
doi:10.4236/ica.2012.34042 fatcat:xjd7oed6cbdxzfk5k2abxxesci

Automated Process Quality Monitoring With Prediction Of Fault Condition Using Measurement Data

Hyun-Woo Cho
2013 Zenodo  
This approach utilizes genetic algorithms (GA) based variable selection, and we evaluate the predictive performance of several prediction methods using real data.  ...  The construction of prediction models for predicting faulty conditions is quite essential in making decisions on when to perform machine maintenance.  ...  In this respect, the GA-SPPCA models for case 1 and case 2 has a better predictive ability than PLS models. The GA-SPPCA models produced the predicted values close to the diagonal line.  ... 
doi:10.5281/zenodo.1058342 fatcat:nkn23uxvdnfq5hyhl6vfkynnwa

Predicting Profile Soil Properties with Reflectance Spectra via Bayesian Covariate-Assisted External Parameter Orthogonalization

Kristen S. Veum, Paul A. Parker, Kenneth A. Sudduth, Scott H. Holan
2018 Sensors  
of (1) traditional PLS analysis, (2) PLS on EPO-transformed spectra (PLS-EPO), (3) PLS-EPO with the Bayesian Lasso (PLS-EPO-BL), and (4) covariate-assisted PLS-EPO-BL models.  ...  For the prediction of soil properties using a model trained on dry spectra and tested on field moist spectra, the PLS-EPO transformation dramatically improved model performance relative to PLS alone, reducing  ...  Figure 5 illustrates the reduction of the coefficient estimates for the Bayesian Lasso model with and without the added covariate for prediction of SOC and clay content.  ... 
doi:10.3390/s18113869 pmid:30423836 fatcat:iyod5diyjjeibaf2opgwxxhrmu

Page 282 of Journal of Chemometrics Vol. 2, Issue 4 [page]

1988 Journal of Chemometrics  
PCR may waste some predictive ability compared to PLS.'?  ...  Unlike PCR, however, PLS considers the ability of the X-block components to optimally predict the Y-variables (via an exchange of scores between X and Y), while PCR only models the covariance in the X-block  ... 

New developments in Sparse PLS regression [article]

Jérémy Magnanensi, Myriam Maumy-Bertrand, Nicolas Meyer, Frédéric Bertrand
2016 arXiv   pre-print
We compare their variable selection reliability and stability concerning tuning parameters determination, as well as their predictive ability, using simulated data for PLS and real microarray gene expression  ...  We observe that our new dynamic bootstrapbased method has the property of best separating random noise in y from the relevant information with respect to other methods, leading to better accuracy and predictive  ...  Acknowledgements Funding: The authors gratefully acknowledge the Labex IRMIA for J. Magnanensi's PhD grant.  ... 
arXiv:1601.03281v1 fatcat:fkxk2ahn45hhpn7brlmtf5szfm

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis

Alamgir Kabir, Md. Jahanur Rahman, Abu Ahmed Shamim, Rolf D. W. Klemm, Alain B. Labrique, Mahbubur Rashid, Parul Christian, Keith P. West, Mahfuzar Rahman
2017 PLoS ONE  
It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates  ...  Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size.  ...  We also compare the performance of PLS regression with PCR and the individual predictive ability of each these two methods.  ... 
doi:10.1371/journal.pone.0189677 pmid:29261760 pmcid:PMC5738092 fatcat:7lrblep66feqjfnahhzt7ycyom

Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs

C.F. Azevedo, M. Nascimento, F.F. Silva, M.D.V. Resende, P.S. Lopes, S.E.F. Guimarães, L.S. Glória
2015 Genetics and Molecular Research  
between the principal and the independent component methods and provided the most accurate genomic breeding estimates for most carcass traits in pigs.  ...  Conflicts of interest The authors declare no conflict of interest. ACKNOWLEDGMENTS Research supported by CAPES and CNPq.  ...  The main difference between PLS and PCR is that the extracted components of PCR explain the variance of covariates (X) and the extracted components of PLS have higher covariance for the response variables  ... 
doi:10.4238/2015.october.9.10 pmid:26505370 fatcat:rcdwqa3harbvjd4rtyqvfaslw4

A comparison of classification methods for differentiating fronto-temporal dementia from Alzheimer's disease using FDG-PET imaging

Roger Higdon, Norman L. Foster, Robert A. Koeppe, Charles S. DeCarli, William J. Jagust, Christopher M. Clark, Nancy R. Barbas, Steven E. Arnold, R. Scott Turner, Judith L. Heidebrink, Satoshi Minoshima
2004 Statistics in Medicine  
We have examined statistical discrimination procedures to help achieve this purpose and compared the results to visual ratings of FDG-PET images.  ...  We performed the data reduction techniques of principal components analysis (PCA) and partial least-squares (PLS) on the entire image and then used linear discriminant analysis (LDA), quadratic (QDA) or  ...  Also, it may be of beneÿt to see if a larger more diverse training set will improve predictive abilities of the models.  ... 
doi:10.1002/sim.1719 pmid:14716732 fatcat:7fxph5vve5c45a2tfexmhhwb4m

Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data

Joram M. Posma, Isabel Garcia-Perez, Timothy M. D. Ebbels, John C. Lindon, Jeremiah Stamler, Paul Elliott, Elaine Holmes, Jeremy K. Nicholson
2018 Journal of Proteome Research  
Metabolism is altered by genetics, diet, disease status, environment, and many other factors. Modeling either one of these is often done without considering the effects of the other covariates.  ...  Using simulated data, we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods.  ...  Low (or negative) Q 2 -values indicate poor model predictive ability and in cases where R 2 is high but Q 2 is low this means the model is overfitting the data.  ... 
doi:10.1021/acs.jproteome.7b00879 pmid:29457906 pmcid:PMC5891819 fatcat:wvqkmudlc5a5fafsrtvdlpm6iq

Selecting both latent and explanatory variables in the PLS1 regression model

Aziz Lazraq, Robert Cléroux, Jean-Pierre Gauchi
2003 Chemometrics and Intelligent Laboratory Systems  
They are also compared empirically to two other methods that exist in the literature with respect to the quality of fit of the model and to their predictive ability.  ...  The significant PLS components are first obtained and the two predictor selection methods, called PLS -Forward and PLS -Bootstrap, are applied to the PLS model obtained.  ...  This paper has been supported, in part, by the National Sciences and Engineering Research Council of Canada.  ... 
doi:10.1016/s0169-7439(03)00027-3 fatcat:udqyp6y7dnci7lojlvubra5dci

An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle

T.K. Belay, B.S. Dagnachew, Z.M. Kowalski, T. Ådnøy
2017 Journal of Dairy Science  
Part of data set 1 was used to calibrate a prediction model (n = 496) and the remaining part of data set 1 (n = 330) was used to validate the calibration models, as well as to evaluate the DP and IP approaches  ...  The main aim of this study was to verify whether mixed modeling of milk spectra in the form of factors scores (DP) gives better prediction of blood β-hydroxybutyrate (BHB) than the univariate approach  ...  The authors also acknowledge Achim Kohler and Valeria Tafintseva (Norwegian University of Life Sciences) for their help in model calibration.  ... 
doi:10.3168/jds.2016-12252 pmid:28571989 fatcat:rssxczo3l5adjbr7ddstzs24ji

Combining Land-Use Regression and Chemical Transport Modeling in a Spatiotemporal Geostatistical Model for Ozone and PM2.5

Meng Wang, Paul D. Sampson, Jianlin Hu, Michael Kleeman, Joshua P. Keller, Casey Olives, Adam A. Szpiro, Sverre Vedal, Joel D. Kaufman
2016 Environmental Science and Technology  
Table S1 -S4, Figure S1 -S3, details of model structure, geographical covariates, and sensitivity analyses on model performances by seasons and site types are provided in the Supporting information.  ...  We present a novel geostatistical modeling framework, incorporating CTM predictions into a spatio-temporal LUR model with spatial smoothing to estimate spatio-temporal variability of ozone (O 3 ) and particulate  ...  Environmental Protection Agency STAR research assistance agreements, RD831697 (MESA Air) and RD833741 (MESA Coarse) awarded to the University of Washington, and award R83386401 to the University of California  ... 
doi:10.1021/acs.est.5b06001 pmid:27074524 pmcid:PMC5096654 fatcat:bptsjhk3indrtfyrhlcstg4c2a

Survival prediction from clinico-genomic models - a comparative study

Hege M Bøvelstad, Ståle Nygård, Ørnulf Borgan
2009 BMC Bioinformatics  
Most of the proposed prediction methods make use of genomic data alone without considering established clinical covariates that often are available and known to have predictive value.  ...  Recent studies suggest that combining clinical and genomic information may improve predictions, but there is a lack of systematic studies on the topic.  ...  The work of SN was supported by NFR via Statistical Analysis of Risk (project number 154079), and by the Norwegian Computing Center. The authors kindly thank A. Oberthür and L.  ... 
doi:10.1186/1471-2105-10-413 pmid:20003386 pmcid:PMC2811121 fatcat:4t7refhuevg63cuozqodkou3ym
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