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Core Statistical Methods for Chemogenomic Data

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Computational Chemogenomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1825))

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Abstract

Chemogenomic modeling involves the construction of algorithmic or statistical models for prediction on new input data and is often based on noisy, multidescriptor data. A deeper understanding of such data through statistical analyses can underpin informed study design and increase information gain from prediction results and model performances. This chapter mediates basic statistical concepts and provides step-by-step instructions to explore and visualize chemogenomic data based on the statistics-centered, open-source software R. Directions on executing essential techniques such as the calculation of correlations, hypothesis testing, and clustering are provided.

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Acknowledgments

Support from the Japan Society for the Promotion of Science (JSPS) in conjunction with the Alexander von Humboldt Foundation (AvH) is gratefully acknowledged.

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Authors and Affiliations

  1. Graduate School of Pharmaceutical Sciences, Yoshida-shimoadachicho, Kyoto University, Sakyo-ku, Kyoto, Japan

    Christin Rakers

  2. Graduate School of Science Nagoya University, Nagoya, Japan

    Christin Rakers

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  1. Christin Rakers
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Correspondence to Christin Rakers .

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Editors and Affiliations

  1. Life Science Informatics Research Unit, Laboratory of Molecular Biosciences, Kyoto University Graduate School of Medicine, Kyoto, Japan

    J.B. Brown

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Rakers, C. (2018). Core Statistical Methods for Chemogenomic Data. In: Brown, J. (eds) Computational Chemogenomics. Methods in Molecular Biology, vol 1825. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8639-2_7

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  • DOI: https://doi.org/10.1007/978-1-4939-8639-2_7

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8638-5

  • Online ISBN: 978-1-4939-8639-2

  • eBook Packages: Springer Protocols

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