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Predictive Modeling of Anti-Cancer Drug Sensitivity from Genetic Characterizations

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Cancer Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1878))

Abstract

Accurately predicting sensitivity of tumor cells to anti-cancer drugs based on genetic characterizations is a significant challenge for personalized cancer therapy. This chapter provides a computational procedure to design predictive models from individual genomic characterizations and combine them to arrive at an integrated predictive model. Integrated modeling employs the complementary information from heterogeneous genetic characterizations to improve the prediction error as well as lowering the error confidence interval.

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Authors and Affiliations

  1. Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA

    Raziur Rahman & Ranadip Pal

Authors
  1. Raziur Rahman
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  2. Ranadip Pal
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Corresponding author

Correspondence to Ranadip Pal .

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Editors and Affiliations

  1. Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    Alexander Krasnitz

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Rahman, R., Pal, R. (2019). Predictive Modeling of Anti-Cancer Drug Sensitivity from Genetic Characterizations. In: Krasnitz, A. (eds) Cancer Bioinformatics. Methods in Molecular Biology, vol 1878. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8868-6_14

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  • DOI: https://doi.org/10.1007/978-1-4939-8868-6_14

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8866-2

  • Online ISBN: 978-1-4939-8868-6

  • eBook Packages: Springer Protocols

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