IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0037018.html
   My bibliography  Save this article

An Improved PSO Algorithm for Generating Protective SNP Barcodes in Breast Cancer

Author

Listed:
  • Li-Yeh Chuang
  • Yu-Da Lin
  • Hsueh-Wei Chang
  • Cheng-Hong Yang

Abstract

Background: Possible single nucleotide polymorphism (SNP) interactions in breast cancer are usually not investigated in genome-wide association studies. Previously, we proposed a particle swarm optimization (PSO) method to compute these kinds of SNP interactions. However, this PSO does not guarantee to find the best result in every implement, especially when high-dimensional data is investigated for SNP–SNP interactions. Methodology/Principal Findings: In this study, we propose IPSO algorithm to improve the reliability of PSO for the identification of the best protective SNP barcodes (SNP combinations and genotypes with maximum difference between cases and controls) associated with breast cancer. SNP barcodes containing different numbers of SNPs were computed. The top five SNP barcode results are retained for computing the next SNP barcode with a one-SNP-increase for each processing step. Based on the simulated data for 23 SNPs of six steroid hormone metabolisms and signalling-related genes, the performance of our proposed IPSO algorithm is evaluated. Among 23 SNPs, 13 SNPs displayed significant odds ratio (OR) values (1.268 to 0.848; p

Suggested Citation

  • Li-Yeh Chuang & Yu-Da Lin & Hsueh-Wei Chang & Cheng-Hong Yang, 2012. "An Improved PSO Algorithm for Generating Protective SNP Barcodes in Breast Cancer," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-9, May.
    • Handle: RePEc:plo:pone00:0037018
    DOI: 10.1371/journal.pone.0037018
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0037018
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0037018&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0037018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

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


    Cited by:

    1. Stephanie Yang & Hsueh-Chih Chen & Chih-Hsien Wu & Meng-Ni Wu & Cheng-Hong Yang, 2021. "Forecasting of the Prevalence of Dementia Using the LSTM Neural Network in Taiwan," Mathematics, MDPI, vol. 9(5), pages 1-19, February.
    2. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    3. Cheng-Hong Yang & Yu-Da Lin & Li-Yeh Chuang & Jin-Bor Chen & Hsueh-Wei Chang, 2013. "MDR-ER: Balancing Functions for Adjusting the Ratio in Risk Classes and Classification Errors for Imbalanced Cases and Controls Using Multifactor-Dimensionality Reduction," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-8, November.
    4. Xinmiao Li & Jing Li & Yukeng Wu, 2015. "A Global Optimization Approach to Multi-Polarity Sentiment Analysis," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-18, April.

    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:plo:pone00:0037018. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.