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A variant of sparse partial least squares for variable selection and data exploration
2014
Frontiers in Neuroinformatics
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When data are sparse and/or predictors multicollinear, current implementation of sparse partial least squares (SPLS) does not give estimates for non-selected predictors nor provide a measure of inference ...
As a result, all-possible SPLS gives more information than the dichotomous output of traditional SPLS, making it useful when undertaking data exploration and hypothesis generation for a large number of ...
A full list of principal CHS investigators and institutions can be found at https://chs-nhlbi.org/pi. ...
doi:10.3389/fninf.2014.00018
pmid:24624079
pmcid:PMC3939647
fatcat:zhoza6nje5hmrixfmvfbzlf4vm
Group-wise partial least square regression
2017
Journal of Chemometrics
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ACKNOWLEDGEMENTS Dr Ewa Szymańska is gratefully acknowledged for making available the MATLAB implementation of the SPLS algorithm. ...
This work is partly supported by the Spanish Ministry of Economy and Competitiveness and FEDER funds through project TIN2014-60346-R and by the EU Commission through the FP7 project INFECT (Contract No ...
The regression coefficients for the PLS, SPLS, and GPLS models are given FIGURE 9 Regression coefficients ( ) for the first 3 latent variables for partial least squares (PLS), sparse partial least squares ...
doi:10.1002/cem.2964
fatcat:pfj6gunpxzdxncytcnstckfxm4
Penalized partial least squares for pleiotropy
2021
BMC Bioinformatics
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The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of variables, and a Lasso penalization that links all independent data sets. ...
Conclusion The joint-sgPLS shows interesting properties for detecting a signal. As an extension of the PLS, the method is suited for data with a large number of variables. ...
Acknowledgements The authors acknowledge Pascal Guénel for providing the breast and thyroid cancer data. ...
doi:10.1186/s12859-021-03968-1
pmid:33627076
fatcat:yjviawoeu5fsfosshtbyu4oyty
A multivariate approach to investigate the combined biological effects of multiple exposures
2018
Journal of Epidemiology and Community Health
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PLS algorithms can easily scale to highdimensional exposures and responses, and prove useful for exposome research to identify sparse sets of exposures jointly affecting a set of (selected) biological ...
To capture both the complexity of the exposome and its possibly pleiotropic effects, models handling multivariate predictors and responses, such as partial least squares (PLS) algorithms, can prove useful ...
Results are presented for the sparse PLS models performing variable selection of both exposures and proteins. PLS, partial least squares. ...
doi:10.1136/jech-2017-210061
pmid:29563153
pmcid:PMC6031275
fatcat:gi26ayb23vfydgflezyny7tqku
Sparse reduced-rank regression for integrating omics data
2020
BMC Bioinformatics
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It also assumes variables for each data type are independent, and thus ignores correlations that exist between variables both within each data type and across the data types. ...
A popular epidemiology approach is to consider an association of each of the predictors with each of the response using a univariate linear regression model, and to select predictors that meet a priori ...
Acknowledgments We are grateful to the Emory Predictive Health Institute for providing us with the genomics, metabolomics, and clinical data used in the real data analysis. ...
doi:10.1186/s12859-020-03606-2
pmid:32620072
fatcat:jzlzt4ng2zc2znjnscjldubv24
Semi-supervised Multivariate Statistical Network Monitoring for Learning Security Threats
2019
IEEE Transactions on Information Forensics and Security
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The supervised learning is based on an extension of the gradient descent method based on Partial Least Squares (PLS). Moreover, we enhance this method by using sparse PLS variants. ...
This paper presents a semi-supervised approach for intrusion detection. ...
ACKNOWLEDGMENT This work is partly supported by the Spanish Ministry of Economy and Competitiveness and FEDER funds through projects TIN2014-60346-R and TIN2017-83494-R. ...
doi:10.1109/tifs.2019.2894358
fatcat:2a7oxvm6d5chvkxqkduvqahife
Temporal prediction of future state occupation in a multistate model from high-dimensional baseline covariates via pseudo-value regression
2016
Figshare
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least squares (PLS) or the least absolute shrinkage and selection operator (LASSO), or their variants. ...
With the advent of high-throughput genomic and proteomic assays, a clinician may intent to use such high-dimensional covariates in making better prediction of state occupation. ...
Sparse partial least squares regression for simultaneous dimension reduction and variable selection. Journal of the Royal Statistical Society, Series B , 3-25. ...
doi:10.6084/m9.figshare.4487504
fatcat:4lfpmcdhrrgztlofepv4occxgq
Predicting temporal lobe volume on MRI from genotypes using L1-L2 regularized regression
2012
2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)
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Penalized or sparse regression methods are gaining increasing attention in imaging genomics, as they can select optimal regressors from a large set of predictors whose individual effects are small or mostly ...
The rs9933137 variant in the RBFOX1 gene was a highly contributory genotype, along with rs10845840 in GRIN2B and rs2456930, discovered previously in a univariate genomewide search. ...
Acknowledgments ADNI data collection was supported by federal and private funds including NIH grants U01 AG024904, P30 AG010129, K01 AG030514, and the Dana Foundation. ...
doi:10.1109/isbi.2012.6235766
pmid:22903144
pmcid:PMC3420969
dblp:conf/isbi/KohannimHJSHTJWT12
fatcat:rbmdrvlwrrctvbpobryuvqnvb4
Practical comparison of sparse methods for classification of Arabica and Robusta coffee species using near infrared hyperspectral imaging
2015
Chemometrics and Intelligent Laboratory Systems
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In the present work sparse-based methods are applied to the analysis of hyperspectral images with the aim at studying their capability of being adequate methods for variable selection in a classification ...
The key aspect of sparse methods is the possibility of performing variable selection by forcing the model coefficients related to irrelevant variables to zero. ...
ACKNOWLEDGMENTS The authors wish to thank Luigi Bellucci (Caffè Molinari spa) for providing the coffee samples and technical support. ...
doi:10.1016/j.chemolab.2015.07.010
fatcat:dgtpqo7qrrg7rly5uvyfhnokgy
Regularized estimation of large-scale gene association networks using graphical Gaussian models
2009
BMC Bioinformatics
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A key issue when the number of variables greatly exceeds the number of samples is the estimation of the matrix of partial correlations. ...
Graphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data. ...
We thank Lukas Meier and Mikio L. Braun for constructive comments on model selection, and Animesh Acharjee for helpful feedback on the R package "parcor". ...
doi:10.1186/1471-2105-10-384
pmid:19930695
pmcid:PMC2808166
fatcat:klorifcsgjezzcywgtfqq6vrpm
Gene-Environment Interaction: A Variable Selection Perspective
[article]
2020
arXiv
pre-print
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Then, after a brief introduction on the variable selection methods, we review penalization and relevant variable selection methods in marginal and joint paradigms respectively under a variety of conceptual ...
Discussions on strengths and limitations, as well as computational aspects of the variable selection methods tailored for G×E studies have also been provided. ...
Acknowledgement We thank the editor and reviewers for their invitation, careful review and insightful comments, leading to a significant improvement of this article. ...
arXiv:2003.02930v1
fatcat:gha2quplljaxnprpv4cfnfmqgi
A new statistical framework for genetic pleiotropic analysis of high dimensional phenotype data
2016
BMC Genomics
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To overcome limitations of the traditional genetic pleiotropic analysis of multiple phenotypes, we develop sparse structural equation models (SEMs) as a general framework for a new paradigm of genetic ...
The widely used genetic pleiotropic analyses of multiple phenotypes are often designed for examining the relationship between common variants and a few phenotypes. ...
Availability of data and materials Exome sequence data were generated from the NHLBI's Exome Sequencing Project (ESP) and have been deposited in dbGaP as part of the ESP cohort data. ...
doi:10.1186/s12864-016-3169-1
pmid:27821073
pmcid:PMC5100198
fatcat:yjq5oafcnbeljbzhbdc7qxjgca
Recent trends in multi-block data analysis in chemometrics for multi-source data integration
2021
TrAC. Trends in analytical chemistry
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Sometimes classical chemometric methods such as principal component analysis (PCA) and partial least squares regression (PLS) are not adequate to analyze this kind of data. ...
Finally, a summary of software resources available for multi-block data analysis is provided. ...
order data Sequential preprocessing through orthogonalization (SPORT) Based on sequential orthogonalized partial least-squares regression Includes a combination of partial least-squares regression and ...
doi:10.1016/j.trac.2021.116206
fatcat:chqzfyrsuzay7cvdw3haybkvhy
Above and beyond state-of-the-art approaches to investigate sequence data: summary of methods and results from the population-based association group at the Genetic Analysis Workshop 19
2016
BMC Genetics
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Estimates of relatedness and population structure strongly depended on the allele frequency of selected variants for inference. ...
Haplotype association methods may constitute a valuable complement of collapsing approaches for sequence data. ...
Justo Lorenzo Bermejo thanks all members of the GAW19 working group Population-Based Association for their participation in the workshop and for the fruitful discussions. ...
doi:10.1186/s12863-015-0310-0
pmid:26866664
pmcid:PMC4895250
fatcat:wyl4savbgjbevhlraqgfs6eu34
The group exponential lasso for bi-level variable selection
2015
Biometrics
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An ideal penalized regression approach would select variables by balancing both the direct evidence of a feature's importance as well as the indirect evidence offered by the grouping structure. ...
Finally, we apply these methods to the problem of detecting rare variants in a genetic association study. ...
A similar result holds, at least for τ = 0.3 and τ = 0.5, with respect to variable selection. ...
doi:10.1111/biom.12300
pmid:25773593
fatcat:x66acnwxvzd2jdx2gtjqfzbp3y
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