Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems

Keywords

Interpretability, Feature selection, Linear discriminant analysis, Computer science, Artificial intelligence

References

• Classification and regression treesAlexander Gordon, Leo Breiman, Jerome H. Friedman — 1983
• Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networksJaved Khan, Jun S. Wei, Markus Ringnér — 2001
• PLS regression methodsAgnar Höskuldsson — 1988
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression DataSandrine Dudoit, Jane Fridlyand, Terence P. Speed — 2002
• Sparse Canonical Correlation Analysis with Application to Genomic Data IntegrationElena Parkhomenko, David Tritchler, Joseph Beyene — 2009
• Multi-class tumor classification by discriminant partial least squares using microarray gene expression data and assessment of classification modelsYongxi Tan, Leming Shi, Weida Tong — 2004
• Efficient multi-class cancer diagnosis algorithm, using a global similarity patternTae Young Yang — 2009
• Addendum: Regularization and variable selection via the elastic netHui Zou, Trevor Hastie — 2005
• Feature selection in omics prediction problems using cat scores and false nondiscovery rate controlMiika Ahdesmäki, Korbinian Strimmer — 2009
• Prediction by Supervised Principal ComponentsEric Bair, Trevor Hastie, Debashis Paul — 2006
• Sparse principal component analysis via regularized low rank matrix approximationHaipeng Shen, Jianhua Z. Huang — 2008
• Genotype, haplotype and copy-number variation in worldwide human populationsMattias Jakobsson, Sonja W. Scholz, Paul Scheet — 2008
• PLS-regression: a basic tool of chemometricsSvante Wold, Michael Sjöstróm, Lennart Eriksson — 2001
• Sparse canonical methods for biological data integration: application to a cross-platform studyKim‐Anh Lê Cao, Pascal G.P. Martin, Christian P. Robert — 2009
• Prediction of central nervous system embryonal tumour outcome based on gene expressionScott L. Pomeroy, Pablo Tamayo, Michelle Gaasenbeek — 2002
• Iteratively reweighted partial least squares estimation for generalized linear regressionBrian D. Marx — 1996
• Classification using partial least squares with penalized logistic regressionGersende Fort, Sophie Lambert‐Lacroix — 2005
• Partial least squares: a versatile tool for the analysis of high-dimensional genomic dataAnne‐Laure Boulesteix, Korbinian Strimmer — 2006
• A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis.Daniela Witten, Robert Tibshirani, Trevor Hastie — 2009
• Gene Ontology: tool for the unification of biologyMichael Ashburner, Catherine A. Ball, Judith A. Blake — 2000

Cited by

• Metabolomics Profile Comparisons of Irradiated and Non-Irradiated Stored Donor Red Blood CellsRavi M. Patel, John D. Roback, Karan Uppal — 2014
• Impact of feed restriction and housing hygiene conditions on specific and inflammatory immune response, the cecal bacterial community and the survival of young rabbits.Sylvie Combes, K. Massip, Océane Martin — 2017
• Probiotics Strains Modulate Gut Microbiota and Lipid Metabolism in Mule DucksA. H. Moubasher — 2018
• Multigene panels in prostate cancer risk assessment.Julian Little, Brenda Wilson, Ron Carter — 2012
• Machine learning and high-performance computing: Infrastructure and algorithms for the genome-scale study of genetic and epigenetic regulatory mechanisms with applications in neurosciencePilib Ó Broin — 2014
• High-Dimensional Sparse Matched Case-Control and Case-Crossover Data: A Review of Recent Works, Description of an R Tool and an Illustration of the Use in Epidemiological StudiesMarta Avalos, Yves Grandvalet, Hélène Pouyes — 2013
• Intégration d'approches génétique et écophysiologique pour l'analyse du dialogue protéines-gènes / environnement dans l'élaboration et le maintien de la texture du fruit de tomateRémy Aurand — 2013
• Practical comparison of sparse methods for classification of Arabica and Robusta coffee species using near infrared hyperspectral imagingRosalba Calvini, Alessandro Ulrici, José Manuel Amigo — 2015
• Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or SorafenibБин Ли, Hyunjin Shin, Georgy Gulbekyan — 2015
• The quest for tolerant varieties: the importance of integrating “omics” techniques to phenotypingMichel Zivy, Stefanie Wienkoop, Jenny Renaut — 2015
• Exploring Sparsity in Multi-class Linear Discriminant AnalysisDong Xia — 2014
• Statistical Modelling of Markers of Severity in Rheumatoid ArthritisL. Taylor — 2014
• Linking metabolomics data to underlying metabolic regulationThomas Nägele — 2014
• Bioinformatic Tools for Next Generation DNA SequencingMads Sønderkær — 2012
• Detecting the Phenology and Discriminating Mediterranean Natural Habitats With Multispectral Sensors—An Analysis Based on Multiseasonal Field SpectraJean‐Baptiste Féret, Christina Corbane, Samuel Alleaume — 2015
• Sparse partial-least-squares discriminant analysis for different geographical origins of Salvia miltiorrhiza by (1) H-NMR-based metabolomics.Miaomiao Jiang, Chunhua Wang, Yu Zhang — 2014
• Pre- and post-mating reproductive barriers drive divergence of five sympatric species of Naryciinae moths (Lepidoptera: Psychidae)Jelmer A. Elzinga, Johanna Mappes, Lauri Kaila — 2014
• Chemometric regression techniques as emerging, powerful tools in genetic association studiesGerard G. Dumancas, Sindhura Ramasahayam, Ghalib Bello — 2015
• Integration and variable selection of ‘omics’ data sets with PLS: a surveyKim‐Anh Lê Cao, Caroline Le Gall — 2011
• Phytochemical investigation of Acronychia species using NMR and LC-MS based dereplication and metabolomics approachesEirini Kouloura — 2014

Related papers

• Improvement Distance Discriminant Analysis MethodLiwen Huang — 2011
• The influence of observations for local log-odd in linear discriminant analysisWing K. Fung — 1996
• Probability and Adjustment of Mistaken Discriminant in Discriminant AnalysisXU Zhao-di — 2007
• Global-local fisher discriminant approach for face recognitionQianqian Gao, Springer-Verlag London — 2014
• The use of the discriminant analysis method for e π μ separation in BESZhijin Jiang, Taijie Wang, Yigang Xie — 1994
• Improved Distance Discriminant Analysis Based on Character Vector Set and Its Application In Criedit Evaluation of CorporationsLiwen Huang — 2011
• A new form of discriminant surfaces using polar coordinatesJacques Duchêne — 1987
• Global–local fisher discriminant approach for face recognitionQianqian Wang, Xiaolei Hu, Quanxue Gao — 2014
• Block-based two-dimensional scatter-difference discriminant analysis and face recognitionCaikou Chen, Li Cao, Meilin Cui — 2008
• Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine LearningLiwen Huang — 2020