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Machine Learning and Combinatorial Optimization to Detect Gene-gene Interactions in Genome-wide Real Data: Looking Through the Prism of Four Methods and Two Protocols

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Biomedical Engineering Systems and Technologies (BIOSTEC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1211))

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Abstract

For most genetic diseases, a wide gap exists between the heritability estimated from familial data and the heritability explained through standard genome-wide association studies. One of the incentive lines of research is epistasis - or gene-gene interaction -. However, epistasis detection poses computational challenges. This paper presents three contributions. Our first contribution aims at filling the lack of feedback on the behaviors of published methods dedicated to epistasis, when applied on real-world genetic data. We designed experiments to compare four published approaches encompassing random forests, Bayesian inference, optimization techniques and Markov blanket learning. We included in the comparison the recently developed approach SMMB-ACO (Stochastic Multiple Markov Blankets with Ant Colony Optimization). We used a published dataset related to Crohn’s disease. We compared the methods in all aspects: running times and memory requirements, numbers of interactions of interest (statistically significant 2-way interactions), p-value distributions, numbers of interaction networks and structure of these networks. Our second contribution assesses whether there is an impact of feature selection, performed upstream epistasis detection, on the previous statistics and distributions. Our third contribution consists in the characterization of SMMB-ACO’s behavior on large-scale real data. We report a great heterogeneity across methods, in all aspects, and highlight weaknesses and strengths for these approaches. Moreover, we conclude that in the case of the Crohn’s disease dataset, feature selection implemented through a random forest-based technique does not allow to increase the proportion of interactions of interest in the outputs.

HB and CS are the two joint first co-authors.

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Acknowledgment

This work was supported by the GRIOTE Research project funded by the Pays de la Loire Region. In this study, we have processed real-world data generated by the Wellcome Trust Case Control Consortium. The experiments reported in this paper were performed at the CCIPL (Centre de Calcul Intensif des Pays de la Loire).

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Authors and Affiliations

  1. Nantes University, LS2N, UMR CNRS 6004, 2 rue de la Houssinière, Nantes, France

    Hugo Boisaubert & Christine Sinoquet

Authors
  1. Hugo Boisaubert
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  2. Christine Sinoquet
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Corresponding author

Correspondence to Christine Sinoquet .

Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Caparica, Portugal

    Ana Roque

  2. Lodz University of Technology, Łódź, Poland

    Arkadiusz Tomczyk

  3. Nice Sophia Antipolis University, Nice Cedex 2, France

    Elisabetta De Maria

  4. University of Bremen, Bremen, Germany

    Felix Putze

  5. University of West Bohemia, Pilsen, Czech Republic

    Roman Moucek

  6. Instituto de Telecomunicações, University of Lisbon, Lisbon, Portugal

    Ana Fred

  7. Universidade Nova de Lisboa, Caparica, Portugal

    Hugo Gamboa

Appendix

Appendix

Table 7. Parameter adjustment for the five methods. (Table published in [3]).
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Boisaubert, H., Sinoquet, C. (2020). Machine Learning and Combinatorial Optimization to Detect Gene-gene Interactions in Genome-wide Real Data: Looking Through the Prism of Four Methods and Two Protocols. In: Roque, A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2019. Communications in Computer and Information Science, vol 1211. Springer, Cham. https://doi.org/10.1007/978-3-030-46970-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-46970-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-46969-6

  • Online ISBN: 978-3-030-46970-2

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