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High-Dimensional Profiling for Computational Diagnosis

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Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1526))

Abstract

New technologies allow for high-dimensional profiling of patients. For instance, genome-wide gene expression analysis in tumors or in blood is feasible with microarrays, if all transcripts are known, or even without this restriction using high-throughput RNA sequencing. Other technologies like NMR finger printing allow for high-dimensional profiling of metabolites in blood or urine. Such technologies for high-dimensional patient profiling represent novel possibilities for molecular diagnostics. In clinical profiling studies, researchers aim to predict disease type, survival, or treatment response for new patients using high-dimensional profiles. In this process, they encounter a series of obstacles and pitfalls. We review fundamental issues from machine learning and recommend a procedure for the computational aspects of a clinical profiling study.

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Authors and Affiliations

  1. Institute of Functional Genomics, University of Regensburg, Regensburg, Germany

    Claudio Lottaz, Wolfram Gronwald, Rainer Spang & Julia C. Engelmann

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  1. Claudio Lottaz
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  2. Wolfram Gronwald
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  3. Rainer Spang
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  4. Julia C. Engelmann
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Correspondence to Claudio Lottaz .

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Editors and Affiliations

  1. Monash University, Melbourne, Victoria, Australia

    Jonathan M. Keith

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Lottaz, C., Gronwald, W., Spang, R., Engelmann, J.C. (2017). High-Dimensional Profiling for Computational Diagnosis. In: Keith, J. (eds) Bioinformatics. Methods in Molecular Biology, vol 1526. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6613-4_12

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  • DOI: https://doi.org/10.1007/978-1-4939-6613-4_12

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