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Molecular Computing Approaches

In: Dimensionality Reduction in Data Science

Author

Listed:
  • Max Garzon

    (The University of Memphis, Computer Science)

  • Sambriddhi Mainali

    (The University of Memphis, Computer Science)

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.

Suggested Citation

  • Max Garzon & Sambriddhi Mainali, 2022. "Molecular Computing Approaches," Springer Books, in: Max Garzon & Ching-Chi Yang & Deepak Venugopal & Nirman Kumar & Kalidas Jana & Lih-Yuan Deng (ed.), Dimensionality Reduction in Data Science, chapter 0, pages 145-167, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-05371-9_7
    DOI: 10.1007/978-3-031-05371-9_7
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