IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v65y2009i1p9-18.html
   My bibliography  Save this article

Integration of Ranked Lists via Cross Entropy Monte Carlo with Applications to mRNA and microRNA Studies

Author

Listed:
  • Shili Lin
  • Jie Ding

Abstract

No abstract is available for this item.

Suggested Citation

  • Shili Lin & Jie Ding, 2009. "Integration of Ranked Lists via Cross Entropy Monte Carlo with Applications to mRNA and microRNA Studies," Biometrics, The International Biometric Society, vol. 65(1), pages 9-18, March.
    • Handle: RePEc:bla:biomet:v:65:y:2009:i:1:p:9-18
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2008.01044.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. DeConde Robert P & Hawley Sarah & Falcon Seth & Clegg Nigel & Knudsen Beatrice & Etzioni Ruth, 2006. "Combining Results of Microarray Experiments: A Rank Aggregation Approach," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-25, June.
    2. L. Margolin, 2005. "On the Convergence of the Cross-Entropy Method," Annals of Operations Research, Springer, vol. 134(1), pages 201-214, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Schimek Michael G. & Švendová Vendula & Budinská Eva & Kugler Karl G. & Ding Jie & Lin Shili, 2015. "TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(3), pages 311-316, June.
    2. Fangyuan Zhang & Jie Ding & Shili Lin, 2017. "Testing for Associations of Opposite Directionality in a Heterogeneous Population," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 137-159, June.
    3. Ding, Jiankun & Han, Deqiang & Yang, Yi, 2018. "Iterative ranking aggregation using quality improvement of subgroup ranking," European Journal of Operational Research, Elsevier, vol. 268(2), pages 596-612.
    4. Giuseppe Jurman & Samantha Riccadonna & Roberto Visintainer & Cesare Furlanello, 2012. "Algebraic Comparison of Partial Lists in Bioinformatics," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-20, May.
    5. Antonio Jiménez-Martín & Eduardo Gallego & Alfonso Mateos & Juan A. Fernández Pozo, 2017. "Restoring a Radionuclide Contaminated Aquatic Ecosystem: A Group Decision Making Problem with Incomplete Information within MAUT Accounting for Veto," Group Decision and Negotiation, Springer, vol. 26(4), pages 653-675, July.
    6. Luisa Cutillo & Annamaria Carissimo & Silvia Figini, 2012. "Network Selection: A Method for Ranked Lists Selection," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-13, August.
    7. Lin Shili, 2010. "Space Oriented Rank-Based Data Integration," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-25, April.
    8. Švendová, Vendula & Schimek, Michael G., 2017. "A novel method for estimating the common signals for consensus across multiple ranked lists," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 122-135.
    9. Antonio D’Ambrosio & Carmela Iorio & Michele Staiano & Roberta Siciliano, 2019. "Median constrained bucket order rank aggregation," Computational Statistics, Springer, vol. 34(2), pages 787-802, June.

    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. Zhengsong Lin & Yuting Wang & Xinyue Ye & Yuxi Wan & Tianjun Lu & Yu Han, 2022. "Effects of Low-Carbon Visualizations in Landscape Design Based on Virtual Eye-Movement Behavior Preference," Land, MDPI, vol. 11(6), pages 1-17, May.
    2. Fangyuan Zhang & Jie Ding & Shili Lin, 2017. "Testing for Associations of Opposite Directionality in a Heterogeneous Population," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 137-159, June.
    3. Quefeng Li & Sijian Wang & Chiang-Ching Huang & Menggang Yu & Jun Shao, 2014. "Meta-analysis based variable selection for gene expression data," Biometrics, The International Biometric Society, vol. 70(4), pages 872-880, December.
    4. Cherif Ben Hamda & Raphael Sangeda & Liberata Mwita & Ayton Meintjes & Siana Nkya & Sumir Panji & Nicola Mulder & Lamia Guizani-Tabbane & Alia Benkahla & Julie Makani & Kais Ghedira & H3ABioNet Consor, 2018. "A common molecular signature of patients with sickle cell disease revealed by microarray meta-analysis and a genome-wide association study," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-21, July.
    5. Ali Eshragh & Jerzy Filar & Michael Haythorpe, 2011. "A hybrid simulation-optimization algorithm for the Hamiltonian cycle problem," Annals of Operations Research, Springer, vol. 189(1), pages 103-125, September.
    6. Antonio Jiménez-Martín & Eduardo Gallego & Alfonso Mateos & Juan A. Fernández Pozo, 2017. "Restoring a Radionuclide Contaminated Aquatic Ecosystem: A Group Decision Making Problem with Incomplete Information within MAUT Accounting for Veto," Group Decision and Negotiation, Springer, vol. 26(4), pages 653-675, July.
    7. Švendová, Vendula & Schimek, Michael G., 2017. "A novel method for estimating the common signals for consensus across multiple ranked lists," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 122-135.
    8. Michele Compare & Michele Bellora & Enrico Zio, 2017. "Aggregation of importance measures for decision making in reliability engineering," Post-Print hal-01652234, HAL.
    9. Nguyen, Hoa T.M. & Chow, Andy H.F. & Ying, Cheng-shuo, 2021. "Pareto routing and scheduling of dynamic urban rail transit services with multi-objective cross entropy method," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    10. Dirk P. Kroese & Sergey Porotsky & Reuven Y. Rubinstein, 2006. "The Cross-Entropy Method for Continuous Multi-Extremal Optimization," Methodology and Computing in Applied Probability, Springer, vol. 8(3), pages 383-407, September.
    11. Luisa Cutillo & Annamaria Carissimo & Silvia Figini, 2012. "Network Selection: A Method for Ranked Lists Selection," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-13, August.

    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:bla:biomet:v:65:y:2009:i:1:p:9-18. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.