Sampling Defective Pathways in Phenotype Prediction Problems via the Fisher’s Ratio Sampler

  • Ana Cernea
  • Juan Luis Fernández-MartínezEmail author
  • Enrique J. deAndrés-Galiana
  • Francisco Javier Fernández-Ovies
  • Zulima Fernández-Muñiz
  • Oscar Alvarez-Machancoses
  • Leorey Saligan
  • Stephen T. Sonis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10814)


In this paper, we introduce the Fisher’s ratio sampler that serves to unravel the defective pathways in highly underdetermined phenotype prediction problems. This sampling algorithm first selects the most discriminatory genes, that are at the same time differentially expressed, and samples the high discriminatory genetic networks with a prior probability that it is proportional to their individual Fisher’s ratio. The number of genes of the different networks is randomly established taking into account the length of the minimum-scale signature of the phenotype prediction problem which is the one that contains the most discriminatory genes with the maximum predictive power. The likelihood of the different networks is established via leave-one-out-cross-validation. Finally, the posterior analysis of the most frequently sampled genes serves to establish the defective biological pathways. This novel sampling algorithm is much faster and simpler than Bayesian Networks. We show its application to a microarray dataset concerning a type of breast cancers with very bad prognosis (TNBC). In these kind of cancers, the breast cancer cells have tested negative for hormone epidermal growth factor receptor 2 (HER-2), estrogen receptors (ER), and progesterone receptors (PR). This lack causes that common treatments like hormone therapy and drugs that target estrogen, progesterone, and HER-2 are ineffective. We believe that the genetic pathways that are identified via the Fisher’s ratio sampler, which are mainly related to signaling pathways, provide new insights about the molecular mechanisms that are involved in this complex disease. The Fisher’s ratio sampler can be also applied to the genetic analysis of other complex diseases.


  1. 1.
    De Andrés Galiana, E.J., Fernández-Martínez, J.L., Sonis, S.: Design of biomedical robots for phenotype prediction problems. J. Comput. Biol. 23(8), 678–692 (2016)CrossRefGoogle Scholar
  2. 2.
    Fernández-Martínez, J.L., Fernández-Muñiz, M.Z., Tompkins, M.J.: On the topography of the cost functional in linear and nonlinear inverse problems. Geophysics 77(1), W1–W15 (2012). Scholar
  3. 3.
    Fernández-Martínez, J.L., Pallero, J.L.G., Fernández-Muñiz, Z., Pedruelo-González, L.M.: From Bayes to Tarantola: new insights to understand uncertainty in inverse problems. J. Appl. Geophys. 98, 62–72 (2013)CrossRefGoogle Scholar
  4. 4.
    De Andrés-Galiana, E.J., Fernández-Martínez, J.L., Sonis, S.: Sensitivity analysis of gene ranking methods in phenotype prediction. J. Biomed. Inf. 64, 255–264 (2016)CrossRefGoogle Scholar
  5. 5.
    Jiang, X., Barmada, M.M., Visweswaran, S.: Identifying genetic interactions in genome-wide data using Bayesian networks. Genet. Epidemiol. 34(6), 575–581 (2010)CrossRefGoogle Scholar
  6. 6.
    Su, C., Andrew, A., Karagas, M.R., Borsuk, M.E.: Using Bayesian networks to discover relations between genes, environment, and disease. BioData Mining 6, 6 (2013)CrossRefGoogle Scholar
  7. 7.
    Liedtke, C., Mazouni, C., et al.: Response to neoadjuvant therapy and long-term survival in patients with triple negative breast cancer. J. Clin. Oncol. 26(8), 1275–1281 (2008)CrossRefGoogle Scholar
  8. 8.
    Jézéquel, P., Loussouarn, D., Guérin-Charbonnel, C., Campion, L., et al.: Gene-expression molecular subtyping of triple-negative breast cancer tumours: importance of immune response. Breast Cancer Res. 17, 43 (2015)CrossRefGoogle Scholar
  9. 9.
    Stelzer, G., Inger, A., Olender, T., Iny-Stein, T., Dalah, I., Harel, A., et al.: GeneDecks: paralog hunting and gene-set distillation with GeneCards annotation. OMICS 13(6), 477 (2009)CrossRefGoogle Scholar
  10. 10.
    Reinbolt, R.E., Sonis, S., Timmers, C.D., Fernández-Martínez, J.L., Cernea, A., de Andrés-Galiana, E.J., Hashemi, S., Miller, K., Pilarski, R., Lustberg, M.B.: Genomic risk prediction of aromatase inhibitor-related arthralgia in patients with breast cancer using a novel machine-learning algorithm. Cancer Med. (2017). Scholar
  11. 11.
    Fernández-Martínez, J.L., deAndrés-Galiana, E.J., Sonis, S.T.: Genomic data integration in chronic lymphocytic leukemia. J. Gene Med. 19 (2017). Scholar
  12. 12.
    Mao, G., Jin, H., Wu, L.: DDX23-Linc00630-HDAC1 axis activates the Notch pathway to promote metastasis. Oncotarget 8(24), 38937–38949 (2017)CrossRefGoogle Scholar
  13. 13.
    Jeon, M., Han, J., Nam, S.J., Lee, J.E., Kim, S.: STC-1 expression is upregulated through an Akt/NF-κB-dependent pathway in triple-negative breast cancer cells. Oncol. Rep. 36(3), 1717–1722 (2016)CrossRefGoogle Scholar
  14. 14.
    Gong, X., Wei, W., Chen, L., Xia, Z., Yu, C.: Comprehensive analysis of long non-coding RNA expression profiles in hepatitis B virus-related hepatocellular carcinoma. Oncotarget 7(27), 42422–42430 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ana Cernea
    • 1
  • Juan Luis Fernández-Martínez
    • 1
    Email author
  • Enrique J. deAndrés-Galiana
    • 1
    • 2
  • Francisco Javier Fernández-Ovies
    • 1
  • Zulima Fernández-Muñiz
    • 1
  • Oscar Alvarez-Machancoses
    • 1
  • Leorey Saligan
    • 3
  • Stephen T. Sonis
    • 4
    • 5
  1. 1.Group of Inverse Problems, Optimization and Machine Learning, Department of MathematicsUniversity of OviedoOviedoSpain
  2. 2.Department of Informatics and Computer ScienceUniversity of OviedoOviedoSpain
  3. 3.National Institute of Nursing ResearchNational Institutes of HealthBethesdaUSA
  4. 4.Primary Endpoint SolutionsWatertownUSA
  5. 5.Brigham and Womens’ Hospital and the Dana-Farber Cancer InstituteBostonUSA

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