Partial least squares: a versatile tool for the analysis of high-dimensional genomic data

By Anne-Laure Boulesteix, Korbinian Strimmer. Briefings Bioinform., 2006

830 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/65988e007db0aa2373fa1c2838833588f7ace2b3

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In Analytical Chemistry

In Molecular Biology

Citations in the corpus

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation (2020, methodology): “[103] and made it publically available within the Partial Least Squares Analyses for Genomics (plsgenomics) R package [104, 105].”
Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network (2015, methodology): “and the \best" subset is chosen to be the summary statistic. The second class is projection approach, which constructs summary statistics by linear or non-linear regression on the candidate set (Boulesteix and Strimmer (2007); Wegmann, Leuenberger and Excoer Learning Summary Statistic for ABC v”
Penalized Partial Least Squares with Applications to B-Spline Transformations and Functional Data (2006, background): “for analyzing chemical data [23], and the success of PLS has lead to applications in other scientific fields such as chemoinformatice, physiology or bioinformatics [37, 35, 2].”
Partial least squares prediction in high-dimensional regression (2019, background): “Boulesteix and Strimmer [3] studied the advantages of PLS regression for the analysis of high-dimensional genomic data, and Nguyen and Rocke [31, 32] proposed it for microarray-based classification.”
Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems (2011, methodology): “Note that this might be wrong from a theoretical point of view, however, it has been previously shown that this works well in practice and many authors have used dummy matrice in PLS for classification [30,37,51,52].”
A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding. (2015, methodology): “This approach has also been extensively used in “omics” related fields, for example: metabolomics [3], proteomics [4], genomics [5,6] and in many other fields which generates large amounts of data such as spectroscopy, as described elsewhere [7].”