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International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics

CIBB 2019: Computational Intelligence Methods for Bioinformatics and Biostatistics pp 108–118Cite as

Random Sample Consensus for the Robust Identification of Outliers in Cancer Data

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12313))

Abstract

Random sample consensus (Ransac) is a technique that has been widely used for modeling data with a large amount of noise. Although successfully employed in areas such as computer vision, extensive testing and applications to clinical data, particularly in oncology, are still lacking. We applied this technique to synthetic and biomedical datasets, publicly available at The Cancer Genome Atlas (TCGA) and the UC Irvine Machine Learning Repository, to identify outliers in the classification of tumor samples. The results obtained by combining Ransac with logistic regression were compared against a baseline classical logistic model. To evaluate the robustness of this method, the original datasets were then perturbed by generating noisy data and by artificially switching the labels. The flagged outlier observations were compared against the misclassifications of the baseline logistic model, along with the evaluation of the overall accuracy of both strategies. Ransac has shown high precision in classifying a subset of core (inlier) observations in the datasets evaluated, while simultaneously identifying the outlier observations, as well as robustness to increasingly perturbed data.

A. Veríssimo and M. B. Lopes—joint first author.

Supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) through projects UIDB/50021/2020 (INESC-ID), UIDB/50022/2020 (LAETA, IDMEC), UID/EEA/50008/2019, SFRH/BD/97415/2013, PREDICT (PTDC/CCI-CIF/29877/2017), MATISSE (DSAIPA/DS/0026/2019) and BINDER (PTDC/CCI-INF/29168/2017).

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Notes

  1. 1.

    https://github.com/sysbiomed/ransac.

  2. 2.

    http://archive.ics.uci.edu/ml.

  3. 3.

    https://cancergenome.nih.gov/.

  4. 4.

    https://github.com/sysbiomed/data-archives/releases/download/ransac/brca.tar.gz.

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Author information

Authors and Affiliations

  1. INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, R. Alves Redol 9, 1000-029, Lisbon, Portugal

    André Veríssimo & Susana Vinga

  2. IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    André Veríssimo & Susana Vinga

  3. Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal

    Marta B. Lopes

  4. Center for the Unknown, Champalimaud Foundation, Av. Brasília, 1400-038, Lisbon, Portugal

    Eunice Carrasquinha

Authors
  1. André Veríssimo
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  2. Marta B. Lopes
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  3. Eunice Carrasquinha
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  4. Susana Vinga
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Corresponding author

Correspondence to Susana Vinga .

Editor information

Editors and Affiliations

  1. University of Bergamo, Bergamo, Italy

    Paolo Cazzaniga

  2. University of Milano-Bicocca, Milan, Italy

    Daniela Besozzi

  3. National Research Council, Segrate, Italy

    Ivan Merelli

  4. Università degli Studi di Trieste, Trieste, Italy

    Luca Manzoni

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Veríssimo, A., Lopes, M.B., Carrasquinha, E., Vinga, S. (2020). Random Sample Consensus for the Robust Identification of Outliers in Cancer Data. In: Cazzaniga, P., Besozzi, D., Merelli, I., Manzoni, L. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2019. Lecture Notes in Computer Science(), vol 12313. Springer, Cham. https://doi.org/10.1007/978-3-030-63061-4_11

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  • DOI: https://doi.org/10.1007/978-3-030-63061-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63060-7

  • Online ISBN: 978-3-030-63061-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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