IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v71y2015i2p428-438.html
   My bibliography  Save this article

miRNA–target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer

Author

Listed:
  • Thierry Chekouo
  • Francesco C. Stingo
  • James D. Doecke
  • Kim-Anh Do

Abstract

No abstract is available for this item.

Suggested Citation

  • Thierry Chekouo & Francesco C. Stingo & James D. Doecke & Kim-Anh Do, 2015. "miRNA–target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer," Biometrics, The International Biometric Society, vol. 71(2), pages 428-438, June.
  • Handle: RePEc:bla:biomet:v:71:y:2015:i:2:p:428-438
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/biom.12266
    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. Wei Pan & Benhuai Xie & Xiaotong Shen, 2010. "Incorporating Predictor Network in Penalized Regression with Application to Microarray Data," Biometrics, The International Biometric Society, vol. 66(2), pages 474-484, June.
    2. Valen E. Johnson & David Rossell, 2012. "Bayesian Model Selection in High-Dimensional Settings," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 649-660, June.
    3. Victor Ambros, 2004. "The functions of animal microRNAs," Nature, Nature, vol. 431(7006), pages 350-355, September.
    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. Shi, Guiling & Lim, Chae Young & Maiti, Tapabrata, 2019. "Model selection using mass-nonlocal prior," Statistics & Probability Letters, Elsevier, vol. 147(C), pages 36-44.
    2. Weibing Li & Thierry Chekouo, 2022. "Bayesian group selection with non-local priors," Computational Statistics, Springer, vol. 37(1), pages 287-302, March.
    3. Yize Zhao & Matthias Chung & Brent A. Johnson & Carlos S. Moreno & Qi Long, 2016. "Hierarchical Feature Selection Incorporating Known and Novel Biological Information: Identifying Genomic Features Related to Prostate Cancer Recurrence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1427-1439, October.
    4. Yang Ni & Veerabhadran Baladandayuthapani & Marina Vannucci & Francesco C. Stingo, 2022. "Bayesian graphical models for modern biological applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 197-225, June.
    5. Thierry Chekouo & Francesco C. Stingo & James D. Doecke & Kim-Anh Do, 2017. "A Bayesian integrative approach for multi-platform genomic data: A kidney cancer case study," Biometrics, The International Biometric Society, vol. 73(2), pages 615-624, 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. Dørum Guro & Snipen Lars & Solheim Margrete & Saebo Solve, 2011. "Smoothing Gene Expression Data with Network Information Improves Consistency of Regulated Genes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-26, August.
    2. Nelson, Kelly P. & Parton, Lee C. & Brown, Zachary S., 2022. "Biofuels policy and innovation impacts: Evidence from biofuels and agricultural patent indicators," Energy Policy, Elsevier, vol. 162(C).
    3. José María Galván-Román & Ángel Lancho-Sánchez & Sergio Luquero-Bueno & Lorena Vega-Piris & Jose Curbelo & Marcos Manzaneque-Pradales & Manuel Gómez & Hortensia de la Fuente & Mara Ortega-Gómez & Javi, 2020. "Usefulness of circulating microRNAs miR-146a and miR-16-5p as prognostic biomarkers in community-acquired pneumonia," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-13, October.
    4. Kshitij Srivastava & Anvesha Srivastava, 2012. "Comprehensive Review of Genetic Association Studies and Meta-Analyses on miRNA Polymorphisms and Cancer Risk," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-1, November.
    5. Faming Liang & Momiao Xiong, 2013. "Bayesian Detection of Causal Rare Variants under Posterior Consistency," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-16, July.
    6. Xing Chen & Jun Yin & Jia Qu & Li Huang, 2018. "MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-24, August.
    7. Yanyan Wang & Yujie Zhang & Chi Pan & Feixia Ma & Suzhan Zhang, 2015. "Prediction of Poor Prognosis in Breast Cancer Patients Based on MicroRNA-21 Expression: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-13, February.
    8. Xing Chen & Li Huang, 2017. "LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction," PLOS Computational Biology, Public Library of Science, vol. 13(12), pages 1-28, December.
    9. Gonzalo García-Donato & María Eugenia Castellanos & Alicia Quirós, 2021. "Bayesian Variable Selection with Applications in Health Sciences," Mathematics, MDPI, vol. 9(3), pages 1-16, January.
    10. Christine Peterson & Francesco C. Stingo & Marina Vannucci, 2015. "Bayesian Inference of Multiple Gaussian Graphical Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 159-174, March.
    11. Ang Li & Yingwei Deng & Yan Tan & Min Chen, 2021. "A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-17, June.
    12. Charlotte Glinge & Sebastian Clauss & Kim Boddum & Reza Jabbari & Javad Jabbari & Bjarke Risgaard & Philipp Tomsits & Bianca Hildebrand & Stefan Kääb & Reza Wakili & Thomas Jespersen & Jacob Tfelt-Han, 2017. "Stability of Circulating Blood-Based MicroRNAs – Pre-Analytic Methodological Considerations," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-16, February.
    13. Liu, Jianyu & Yu, Guan & Liu, Yufeng, 2019. "Graph-based sparse linear discriminant analysis for high-dimensional classification," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 250-269.
    14. Andrés Ramírez-Hassan, 2020. "Dynamic variable selection in dynamic logistic regression: an application to Internet subscription," Empirical Economics, Springer, vol. 59(2), pages 909-932, August.
    15. Alexander Link & Verena Becker & Ajay Goel & Thomas Wex & Peter Malfertheiner, 2012. "Feasibility of Fecal MicroRNAs as Novel Biomarkers for Pancreatic Cancer," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-9, August.
    16. Hossain Ahmed & Beyene Joseph, 2013. "Estimation of weighted log partial area under the ROC curve and its application to MicroRNA expression data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(6), pages 743-755, December.
    17. Xueying Tang & Xiaofan Xu & Malay Ghosh & Prasenjit Ghosh, 2018. "Bayesian Variable Selection and Estimation Based on Global-Local Shrinkage Priors," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(2), pages 215-246, August.
    18. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
    19. Hai Lian & Lei Wang & Jingmin Zhang, 2012. "Increased Risk of Breast Cancer Associated with CC Genotype of Has-miR-146a Rs2910164 Polymorphism in Europeans," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-7, February.
    20. Fabricio F Costa & Jared M Bischof & Elio F Vanin & Rishi R Lulla & Min Wang & Simone T Sredni & Veena Rajaram & Maria de Fátima Bonaldo & Deli Wang & Stewart Goldman & Tadanori Tomita & Marcelo B Soa, 2011. "Identification of MicroRNAs as Potential Prognostic Markers in Ependymoma," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-10, October.

    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:71:y:2015:i:2:p:428-438. 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.