IDEAS home Printed from https://ideas.repec.org/p/aiz/louvad/2019020.html
   My bibliography  Save this paper

Feature Selection in metabolomics with PLS-derived methods

Author

Listed:
  • Martin, Manon
  • Govaerts, Bernadette

Abstract

No abstract is available for this item.

Suggested Citation

  • Martin, Manon & Govaerts, Bernadette, 2019. "Feature Selection in metabolomics with PLS-derived methods," LIDAM Discussion Papers ISBA 2019020, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2019020
    as

    Download full text from publisher

    File URL: https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A219770/datastream/PDF_01/view
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Feraud, Baptiste & Munaut, Carine & Martin, Manon & Verleysen, Michel & Govaerts, Bernadette, 2017. "Combining strong sparsity and competitive predictive power with the L-sOPLS approach for biomarker discovery in metabolomics," LIDAM Reprints ISBA 2017045, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Prasad Naik & Chih‐Ling Tsai, 2000. "Partial least squares estimator for single‐index models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 763-771.
    3. Marine Jeanmougin & Aurelien de Reynies & Laetitia Marisa & Caroline Paccard & Gregory Nuel & Mickael Guedj, 2010. "Should We Abandon the t-Test in the Analysis of Gene Expression Microarray Data: A Comparison of Variance Modeling Strategies," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-9, September.
    4. Florian Rohart & Benoît Gautier & Amrit Singh & Kim-Anh Lê Cao, 2017. "mixOmics: An R package for ‘omics feature selection and multiple data integration," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-19, November.
    5. Feraud, Baptiste & Munaut, Carine & Martin, Manon & Verleysen, Michel & Govaerts, Bernadette, 2017. "Combining strong sparsity and competitive predictive power with the L-sOPLS approach for biomarker discovery in metabolomics," LIDAM Discussion Papers ISBA 2017020, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Hyonho Chun & Sündüz Keleş, 2010. "Sparse partial least squares regression for simultaneous dimension reduction and variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 3-25, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dmitry Kobak & Yves Bernaerts & Marissa A. Weis & Federico Scala & Andreas S. Tolias & Philipp Berens, 2021. "Sparse reduced‐rank regression for exploratory visualisation of paired multivariate data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 980-1000, August.
    2. Feraud, Baptiste & Leenders, Justine & Martineau, Estelle & Giraudeau, Patrick & Govaerts, Bernadette & de Tullio, Pascal, 2018. "Two data pre-processing workflows to facilitate the discovery of biomarkers by 2D NMR metabolomics," LIDAM Discussion Papers ISBA 2018016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Bousebata, Meryem & Enjolras, Geoffroy & Girard, Stéphane, 2023. "Extreme partial least-squares," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
    4. Vanessa R. Marcelino & Caitlin Welsh & Christian Diener & Emily L. Gulliver & Emily L. Rutten & Remy B. Young & Edward M. Giles & Sean M. Gibbons & Chris Greening & Samuel C. Forster, 2023. "Disease-specific loss of microbial cross-feeding interactions in the human gut," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Gaoxiang Zhu & Dengfeng Gao & Linzi Li & Yixuan Yao & Yingjie Wang & Minglei Zhi & Jinying Zhang & Xinze Chen & Qianqian Zhu & Jie Gao & Tianzhi Chen & Xiaowei Zhang & Tong Wang & Suying Cao & Aijin M, 2023. "Generation of three-dimensional meat-like tissue from stable pig epiblast stem cells," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    6. Julieta Fuentes & Pilar Poncela & Julio Rodríguez, 2015. "Sparse Partial Least Squares in Time Series for Macroeconomic Forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 576-595, June.
    7. Stamer, Vincent, 2022. "Thinking Outside the Container: A Sparse Partial Least Squares Approach to Forecasting Trade Flows," VfS Annual Conference 2022 (Basel): Big Data in Economics 264096, Verein für Socialpolitik / German Economic Association.
    8. Bin Li & Hyunjin Shin & Georgy Gulbekyan & Olga Pustovalova & Yuri Nikolsky & Andrew Hope & Marina Bessarabova & Matthew Schu & Elona Kolpakova-Hart & David Merberg & Andrew Dorner & William L Trepicc, 2015. "Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-20, June.
    9. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. Lai, Peng & Wang, Qihua & Lian, Heng, 2012. "Bias-corrected GEE estimation and smooth-threshold GEE variable selection for single-index models with clustered data," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 422-432.
    11. Tommaso Proietti, 2016. "On the Selection of Common Factors for Macroeconomic Forecasting," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 593-628, Emerald Group Publishing Limited.
    12. Naik, Prasad A. & Tsai, Chih-Ling, 2004. "Isotonic single-index model for high-dimensional database marketing," Computational Statistics & Data Analysis, Elsevier, vol. 47(4), pages 775-790, November.
    13. Ceren Kolsarici & Demetrios Vakratsas, 2015. "Correcting for Misspecification in Parameter Dynamics to Improve Forecast Accuracy with Adaptively Estimated Models," Management Science, INFORMS, vol. 61(10), pages 2495-2513, October.
    14. Dhiman, Neeraj & Jamwal, Mohit & Kumar, Ajay, 2023. "Enhancing value in customer journey by considering the (ad)option of artificial intelligence tools," Journal of Business Research, Elsevier, vol. 167(C).
    15. Pang, Zhen & Xue, Liugen, 2012. "Estimation for the single-index models with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1837-1853.
    16. Kawano, Shuichi & Fujisawa, Hironori & Takada, Toyoyuki & Shiroishi, Toshihiko, 2015. "Sparse principal component regression with adaptive loading," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 192-203.
    17. Debamita Kundu & Riten Mitra & Jeremy T. Gaskins, 2021. "Bayesian variable selection for multioutcome models through shared shrinkage," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 295-320, March.
    18. Zhang, Ruoyang & Ghosh, Malay, 2022. "Ultra high-dimensional multivariate posterior contraction rate under shrinkage priors," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    19. Hansheng Wang & Chih‐Ling Tsai, 2009. "‘Model selection for generalized linear models with factor‐augmented predictors’," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 241-242, May.
    20. Shengkun Xie & Rebecca Luo, 2022. "Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions," Mathematics, MDPI, vol. 10(10), pages 1-19, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aiz:louvad:2019020. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Nadja Peiffer (email available below). General contact details of provider: https://edirc.repec.org/data/isuclbe.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.