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Binary Classification for Failure Risk Assessment

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Translational Bioinformatics for Therapeutic Development

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2194))

Abstract

Survival analysis is tremendously powerful, and is a popular methodology for analyzing time to event models in bioinformatics. Furthermore, several of its extensions can simultaneously perform variable selection in conjunction with model estimation. While this flexibility is extremely desirable, under certain scenarios, binary class variable selection and classification methods might provide more reliable risk estimates. Synthetic simulations and real data case studies suggest that when (1) randomly censored points comprise only a small portion of data, (2) biological markers are weak, (3) it is desired to compute risk across predetermined time intervals, and (4) the assumptions of the competing time to event models are violated, binary class models tend to perform superior. In practice, it might be prudent to test both model families to guarantee adequate analysis. Here we describe the pipeline of binary class feature selection and classification for time to event risk assessment.

The authors Ali Foroughi pour and Ian Loveless contributed equally to this work.

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Notes

  1. 1.

    QIAGEN Inc., https://www.qiagenbio-informatics.com/products/ingenuity-pathway-analysis.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA

    Ali Foroughi Pour

  2. Department of Mathematics, The Ohio State University, Columbus, OH, USA

    Ali Foroughi Pour

  3. College of Public Health, The Ohio State University, Columbus, OH, USA

    Ian Loveless

  4. Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA

    Maciej Pietrzak

  5. Department of Mathematics, The Ohio State University, Columbus, OH, USA

    Grzegorz Rempala

  6. College of Public Health, The Ohio State University, Columbus, OH, USA

    Grzegorz Rempala

Authors
  1. Ali Foroughi Pour
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  2. Ian Loveless
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  3. Grzegorz Rempala
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  4. Maciej Pietrzak
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Corresponding author

Correspondence to Maciej Pietrzak .

Editor information

Editors and Affiliations

  1. Department of Cutaneous Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA

    Joseph Markowitz

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Foroughi Pour, A., Loveless, I., Rempala, G., Pietrzak, M. (2021). Binary Classification for Failure Risk Assessment. In: Markowitz, J. (eds) Translational Bioinformatics for Therapeutic Development. Methods in Molecular Biology, vol 2194. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0849-4_6

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  • DOI: https://doi.org/10.1007/978-1-0716-0849-4_6

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0848-7

  • Online ISBN: 978-1-0716-0849-4

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