IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v81y2011i7p759-766.html
   My bibliography  Save this article

On the use of double cross-validation for the combination of proteomic mass spectral data for enhanced diagnosis and prediction

Author

Listed:
  • Mertens, B.J.A.
  • van der Burgt, Y.E.M.
  • Velstra, B.
  • Mesker, W.E.
  • Tollenaar, R.A.E.M.
  • Deelder, A.M.

Abstract

We consider a proteomic mass spectrometry case-control study for the calibration of a diagnostic rule for the detection of early-stage breast cancer. For each patient, a pair of two distinct mass spectra is recorded, each of which is derived from a different prior fractionation procedure on the available patient serum. We propose a procedure for combining the distinct spectral expressions from patients for the calibration of a diagnostic discriminant rule. This is achieved by first calibrating two distinct prediction rules separately, each on only one of the two available spectral data sources. A double cross-validatory approach is used to summarize the available spectral data using the two classifiers to posterior class probabilities, on which a combined predictor can be calibrated.

Suggested Citation

  • Mertens, B.J.A. & van der Burgt, Y.E.M. & Velstra, B. & Mesker, W.E. & Tollenaar, R.A.E.M. & Deelder, A.M., 2011. "On the use of double cross-validation for the combination of proteomic mass spectral data for enhanced diagnosis and prediction," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 759-766, July.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:7:p:759-766
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(11)00083-6
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ruedi Aebersold & Matthias Mann, 2003. "Mass spectrometry-based proteomics," Nature, Nature, vol. 422(6928), pages 198-207, March.
    2. B. J. A. Mertens, 2001. "Downdating: Interdisciplinary Research Between Statistics and Computing," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(3), pages 358-366, November.
    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. Alexander Kaever & Manuel Landesfeind & Kirstin Feussner & Burkhard Morgenstern & Ivo Feussner & Peter Meinicke, 2014. "Meta-Analysis of Pathway Enrichment: Combining Independent and Dependent Omics Data Sets," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-12, February.
    2. Dayle L Sampson & Tony J Parker & Zee Upton & Cameron P Hurst, 2011. "A Comparison of Methods for Classifying Clinical Samples Based on Proteomics Data: A Case Study for Statistical and Machine Learning Approaches," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-11, September.
    3. Jiang Tan & Hui-Zhen Fu & Yuh-Shan Ho, 2014. "A bibliometric analysis of research on proteomics in Science Citation Index Expanded," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1473-1490, February.
    4. Jacques Colinge & Keiryn L Bennett, 2007. "Introduction to Computational Proteomics," PLOS Computational Biology, Public Library of Science, vol. 3(7), pages 1-10, July.
    5. J P C Kleijnen & W C M van Beers, 2004. "Application-driven sequential designs for simulation experiments: Kriging metamodelling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(8), pages 876-883, August.
    6. Yun Xu & Wolfgang Schrader, 2021. "Studying the Complexity of Biomass Derived Biofuels," Energies, MDPI, vol. 14(8), pages 1-13, April.
    7. Łuksza Marta & Kluge Bogusław & Ostrowski Jerzy & Karczmarski Jakub & Gambin Anna, 2009. "Two-Stage Model-Based Clustering for Liquid Chromatography Mass Spectrometry Data Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-34, February.
    8. Patrick Leopold Rüther & Immanuel Mirnes Husic & Pernille Bangsgaard & Kristian Murphy Gregersen & Pernille Pantmann & Milena Carvalho & Ricardo Miguel Godinho & Lukas Friedl & João Cascalheira & Albe, 2022. "SPIN enables high throughput species identification of archaeological bone by proteomics," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    9. Ling Li & Mingming Niu & Alyssa Erickson & Jie Luo & Kincaid Rowbotham & Kai Guo & He Huang & Yuxin Li & Yi Jiang & Junguk Hur & Chunyu Liu & Junmin Peng & Xusheng Wang, 2022. "SMAP is a pipeline for sample matching in proteogenomics," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    10. Brar, D.S. & Mackill, D.J. & Hardy, Bill (ed.), 2007. "Rice Genetics V- Proceedings of the Fifth International Rice Genetics Symposium," IRRI Books, International Rice Research Institute (IRRI), number 164486.
    11. Benjamin A Shoemaker & Anna R Panchenko, 2007. "Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners," PLOS Computational Biology, Public Library of Science, vol. 3(4), pages 1-7, April.
    12. Stephan Krueger & Patrick Giavalisco & Leonard Krall & Marie-Caroline Steinhauser & Dirk Büssis & Bjoern Usadel & Ulf-Ingo Flügge & Alisdair R Fernie & Lothar Willmitzer & Dirk Steinhauser, 2011. "A Topological Map of the Compartmentalized Arabidopsis thaliana Leaf Metabolome," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-16, March.
    13. Kertcher, Zack & Venkatraman, Rohan & Coslor, Erica, 2020. "Pleasingly parallel: Early cross-disciplinary work for innovation diffusion across boundaries in grid computing," Journal of Business Research, Elsevier, vol. 116(C), pages 581-594.
    14. Naomi S Hachiya, 2017. "Unfoldin, A Novel Tool for the Analysis of Protein Misfolding or Neurodegenerative Diseases," Open Access Journal of Neurology & Neurosurgery, Juniper Publishers Inc., vol. 6(3), pages 40-44, October.
    15. Guler, Arzu Tugce & Waaijer, Cathelijn J.F. & Mohammed, Yassene & Palmblad, Magnus, 2016. "Automating bibliometric analyses using Taverna scientific workflows: A tutorial on integrating Web Services," Journal of Informetrics, Elsevier, vol. 10(3), pages 830-841.
    16. Lei Xin & Rui Qiao & Xin Chen & Hieu Tran & Shengying Pan & Sahar Rabinoviz & Haibo Bian & Xianliang He & Brenton Morse & Baozhen Shan & Ming Li, 2022. "A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    17. Tianhai Tian & Jiangning Song, 2012. "Mathematical Modelling of the MAP Kinase Pathway Using Proteomic Datasets," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-12, August.
    18. Karsten Suhre & Guhan Ram Venkataraman & Harendra Guturu & Anna Halama & Nisha Stephan & Gaurav Thareja & Hina Sarwath & Khatereh Motamedchaboki & Margaret K. R. Donovan & Asim Siddiqui & Serafim Batz, 2024. "Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    19. Jinfeng Zou & Guini Hong & Xinwu Guo & Lin Zhang & Chen Yao & Jing Wang & Zheng Guo, 2011. "Reproducible Cancer Biomarker Discovery in SELDI-TOF MS Using Different Pre-Processing Algorithms," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-9, October.
    20. Wei Feng & Joanne C. Beer & Qinyu Hao & Ishara S. Ariyapala & Aparna Sahajan & Andrei Komarov & Katie Cha & Mason Moua & Xiaolei Qiu & Xiaomei Xu & Shweta Iyengar & Thu Yoshimura & Rajini Nagaraj & Li, 2023. "NULISA: a proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

    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:eee:stapro:v:81:y:2011:i:7:p:759-766. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.