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Application of Machine-Learning Methods to Understand Gene Expression Regulation

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Book cover Genetic Programming Theory and Practice XII

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

With the development and application of high-throughput technologies, an enormous amount of biological data has been produced in the past few years. These large-scale datasets make it possible and necessary to implement machine learning techniques for mining biological insights. In this chapter, we describe several examples to show how machine learning approaches are used to elucidate the mechanism of transcriptional regulation mediated by transcription factors and histone modifications. We demonstrate that machine learning provides powerful tools to quantitatively relate gene expression with transcription factor binding and histone modifications, to identify novel regulatory DNA elements in the genomes, and to predict gene functions. We also discuss the advantages and limitations of genetic programming in analyzing and processing biological data.

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Authors and Affiliations

  1. Department of Genetics, Institute for Quantitative Biomedical Sciences, and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Hanover, NH, USA

    Chao Cheng

  2. Evolution Enterprises, 48160, 214 W. Main St., Milan, MI, USA

    William P. Worzel

Authors
  1. Chao Cheng
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  2. William P. Worzel
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Corresponding author

Correspondence to Chao Cheng .

Editor information

Editors and Affiliations

  1. Center for the Study of Complex Systems (CSCS), University of Michigan, Ann Arbor, Michigan, USA

    Rick Riolo

  2. Evolution Enterprises, Milan, Michigan, USA

    William P. Worzel

  3. Evolved Analytics, Midland, Michigan, USA

    Mark Kotanchek

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Cheng, C., Worzel, W. (2015). Application of Machine-Learning Methods to Understand Gene Expression Regulation. In: Riolo, R., Worzel, W., Kotanchek, M. (eds) Genetic Programming Theory and Practice XII. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-16030-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-16030-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16029-0

  • Online ISBN: 978-3-319-16030-6

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