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Cells as the first data scientists.

Michael L Wong1,2, Anirudh Prabhu1

  • 1Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC 20015, USA.

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|February 8, 2023
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Summary
This summary is machine-generated.

Data science principles mirror biological information processing. This study frames data science activities within life's evolution, using informatics to compare living and non-living systems.

Keywords:
data sciencedefinitions of lifeevolutioninformaticsinformation

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Area of Science:

  • Bioinformatics
  • Data Science
  • Systems Biology

Background:

  • Life processes environmental information similarly to data science.
  • The information life cycle in data science parallels biological data handling.
  • The data-information-knowledge ecosystem provides a framework for life-environment interactions.

Purpose of the Study:

  • To explore the parallels between data science concepts and biological information processing.
  • To position data science activities within the broader context of life's evolution.
  • To investigate the utility of informatics frameworks in comparing living and abiotic systems.

Main Methods:

  • Conceptual comparison of data science principles with biological information processing.
  • Application of the information life cycle and data-information-knowledge ecosystem models.
  • Utilizing informatics frameworks for systems analysis.

Main Results:

  • Data science concepts like the information life cycle and data-information-knowledge ecosystem are highly analogous to biological processes.
  • Data scientists' activities can be viewed as a continuation of life's evolutionary drive to understand its environment.
  • Informatics frameworks offer a valuable lens for comparing complex evolving systems, both living and abiotic.

Conclusions:

  • Data science offers a powerful conceptual toolkit for understanding fundamental biological information processing.
  • Viewing data science through a biological lens highlights its evolutionary significance.
  • Informatics provides a unified approach to studying diverse complex systems.