IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v69y2017i3d10.1007_s10898-017-0530-0.html
   My bibliography  Save this article

A mixed integer programming-based global optimization framework for analyzing gene expression data

Author

Listed:
  • Giovanni Felici

    (IASI-CNR)

  • Kumar Parijat Tripathi

    (ICAR-CNR)

  • Daniela Evangelista

    (ICAR-CNR)

  • Mario Rosario Guarracino

    (ICAR-CNR)

Abstract

The analysis of high throughput gene expression patients/controls experiments is based on the determination of differentially expressed genes according to standard statistical tests. A typical bioinformatics approach to this problem is composed of two separate steps: first, a subset of genes with altered expression level is identified; then the pathways which are statistically enriched by those genes are selected, assuming they play a relevant role for the biological condition under study. Often, the set of selected pathways contains elements that are not related to the condition. This is due to the fact that the statistical significance is not sufficient for biological relevance. To overcome these problems, we propose a method based on a large mixed integer program that implements a new feature selection model to simultaneously identify the genes whose over- and under-expressions, combined together, discriminate different cancer subtypes, as well as the pathways that are enriched by these genes. The innovation in this model is the solutions are driven towards the enrichment of pathways. That may indeed introduce a bias in the search; such a bias is counter-balanced by a wide exploration of the solution space, varying the involved parameters in their feasible region, and then using a global optimization approach. The conjoint analysis of the pool of solutions obtained by this exploration should indeed provide a robust final set of genes and pathways, overcoming the potential drawbacks of relying solely on statistical significance. Experimental results on transcriptomes for different types of cancer from the Cancer Genome Atlas are presented. The method is able to identify crisp relations between the considered subtypes of cancer and few selected pathways, eventually validated by the biological analysis.

Suggested Citation

  • Giovanni Felici & Kumar Parijat Tripathi & Daniela Evangelista & Mario Rosario Guarracino, 2017. "A mixed integer programming-based global optimization framework for analyzing gene expression data," Journal of Global Optimization, Springer, vol. 69(3), pages 727-744, November.
    • Handle: RePEc:spr:jglopt:v:69:y:2017:i:3:d:10.1007_s10898-017-0530-0
    DOI: 10.1007/s10898-017-0530-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10898-017-0530-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10898-017-0530-0?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
    ---><---

    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. M. R. Guarracino & S. Cuciniello & P. M. Pardalos, 2009. "Classification and Characterization of Gene Expression Data with Generalized Eigenvalues," Journal of Optimization Theory and Applications, Springer, vol. 141(3), pages 533-545, June.
    2. Bertolazzi, P. & Felici, G. & Festa, P. & Fiscon, G. & Weitschek, E., 2016. "Integer programming models for feature selection: New extensions and a randomized solution algorithm," European Journal of Operational Research, Elsevier, vol. 250(2), pages 389-399.
    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. Manlio Gaudioso & Giovanni Giallombardo & Giovanna Miglionico, 2023. "Sparse optimization via vector k-norm and DC programming with an application to feature selection for support vector machines," Computational Optimization and Applications, Springer, vol. 86(2), pages 745-766, November.
    2. Daehan Won & Hasan Manzour & Wanpracha Chaovalitwongse, 2020. "Convex Optimization for Group Feature Selection in Networked Data," INFORMS Journal on Computing, INFORMS, vol. 32(1), pages 182-198, January.
    3. Li, An-Da & He, Zhen & Wang, Qing & Zhang, Yang, 2019. "Key quality characteristics selection for imbalanced production data using a two-phase bi-objective feature selection method," European Journal of Operational Research, Elsevier, vol. 274(3), pages 978-989.
    4. Zhang, Yishi & Zhu, Ruilin & Chen, Zhijun & Gao, Jie & Xia, De, 2021. "Evaluating and selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data," European Journal of Operational Research, Elsevier, vol. 290(1), pages 235-247.
    5. Ghaddar, Bissan & Naoum-Sawaya, Joe, 2018. "High dimensional data classification and feature selection using support vector machines," European Journal of Operational Research, Elsevier, vol. 265(3), pages 993-1004.
    6. Douek-Pinkovich, Yifat & Ben-Gal, Irad & Raviv, Tal, 2022. "The stochastic test collection problem: Models, exact and heuristic solution approaches," European Journal of Operational Research, Elsevier, vol. 299(3), pages 945-959.
    7. Yifat Douek-Pinkovich & Irad Ben-Gal & Tal Raviv, 2021. "The generalized test collection problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 372-386, July.
    8. Jiménez-Cordero, Asunción & Morales, Juan Miguel & Pineda, Salvador, 2021. "A novel embedded min-max approach for feature selection in nonlinear Support Vector Machine classification," European Journal of Operational Research, Elsevier, vol. 293(1), pages 24-35.

    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:spr:jglopt:v:69:y:2017:i:3:d:10.1007_s10898-017-0530-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.