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Molecular Computing Approaches

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Dimensionality Reduction in Data Science

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Abstract

Molecular approaches exploit structural properties built deep into DNA by millions of years of evolution on Earth to code and/or extract some significant features from raw datasets for the purpose of extreme dimensionality reduction and solution efficiency. After describing the deep structure, it is leveraged to render several variations of this theme. They can be used obviously with genomic data, but perhaps surprisingly, with ordinary abiotic data just as well. Two major families of techniques of this kind are reviewed, namely genomic and pmeric coordinate systems for dimensionality reduction and data analysis.

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Author information

Authors and Affiliations

  1. Computer Science, The University of Memphis, Memphis, TN, USA

    Max Garzon & Sambriddhi Mainali

Authors
  1. Max Garzon
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  2. Sambriddhi Mainali
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Corresponding author

Correspondence to Max Garzon .

Editor information

Editors and Affiliations

  1. 375 Dunn Hall, The University of Memphis, Memphis, TN, USA

    Max Garzon

  2. 375 Dunn Hall, The University of Memphis, Memphis, TN, USA

    Ching-Chi Yang

  3. 375 Dunn Hall, The University of Memphis, Memphis, TN, USA

    Deepak Venugopal

  4. 375 Dunn Hall, The University of Memphis, Memphis, TN, USA

    Nirman Kumar

  5. Memphis, TN, USA

    Kalidas Jana

  6. 375 Dunn Hall, The University of Memphis, Memphis, TN, USA

    Lih-Yuan Deng

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Garzon, M., Mainali, S. (2022). Molecular Computing Approaches. In: Garzon, M., Yang, CC., Venugopal, D., Kumar, N., Jana, K., Deng, LY. (eds) Dimensionality Reduction in Data Science. Springer, Cham. https://doi.org/10.1007/978-3-031-05371-9_7

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