Complexity data science: A spin-off from digital twins

Frank Emmert-Streib; Hocine Cherifi; Kimmo Kaski; Stuart Kauffman; Olli Yli-Harja

doi:10.1093/pnasnexus/pgae456

Complexity data science: A spin-off from digital twins

Frank Emmert-Streib, Hocine Cherifi, Kimmo Kaski, Stuart Kauffman, Olli Yli-Harja

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Abstract

Digital twins offer a new and exciting framework that has recently attracted significant interest in fields such as oncology, immunology, and cardiology. The basic idea of a digital twin is to combine simulation and learning to create a virtual model of a physical object. In this paper, we explore how the concept of digital twins can be generalized into a broader, overarching field. From a theoretical standpoint, this generalization is achieved by recognizing that the duality of a digital twin fundamentally connects complexity science with data science, leading to the emergence of complexity data science as a synthesis of the two. We examine the broader implications of this field, including its historical roots, challenges, and opportunities.

Original language	English
Article number	pgae456
Number of pages	7
Journal	PNAS Nexus
Volume	3
Issue number	11
DOIs	https://doi.org/10.1093/pnasnexus/pgae456
Publication status	Published - Nov 2024
Publication type	A1 Journal article-refereed

Funding

This work was supported by the Academy of Finland (352266 to F.E.-S. and 352263 to O.Y.-H.). The funders had no role in study design, data collection and analysis, publication decision, or manuscript preparation.

Funders	Funder number
Research Council of Finland	352263, 352266
Research Council of Finland

Keywords

complexity science
data science
digital twin
learning
simulation

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

General

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10.1093/pnasnexus/pgae456Licence: CC BY

pgae456Final published version, 666 KBLicence: CC BY

https://urn.fi/URN:NBN:fi:tuni-2024120310731Licence: CC BY

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Complexity data science: A spin-off from digital twins

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