Localizing synergies of hidden factors in complex systems : resting brain networks and hela geneexpression profile as case studies
- Author
- Marlis Ontivero-Ortega, Gorana Mijatovic, Luca Faes, Fernando E. Rosas, Daniele Marinazzo (UGent) and Sebastiano Stramaglia
- Organization
- Abstract
- Factor analysis is a well-known statistical method to describe the variability of observed variables in terms of a smaller number of unobserved latent variables called factors. Even though latent factors are conceptually independent of each other, their influence on the observed variables is often joint and synergistic. We propose to quantify the synergy of the joint influence of factors on the observed variables using O-information, a recently introduced metric to assess high-order dependencies in complex systems; in the proposed framework, latent factors and observed variables are jointly analyzed in terms of their joint informational character. Two case studies are reported: analyzing resting fMRI data, we find that DMN and FP networks show the highest synergy, consistent with their crucial role in higher cognitive functions; concerning HeLa cells, we find that the most synergistic gene is STK-12 (AURKB), suggesting that this gene is involved in controlling the HeLa cell cycle. We believe that our approach, representing a bridge between factor analysis and the field of high-order interactions, will find wide application across several domains.
- Keywords
- information theory, factor analysis, latent factors, high order interactions, synergy, resting-state fMRI, cell gene expression
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01K22P2Y0CXCVWSH06PRQPBCD9
- MLA
- Ontivero-Ortega, Marlis, et al. “Localizing Synergies of Hidden Factors in Complex Systems : Resting Brain Networks and Hela Geneexpression Profile as Case Studies.” ENTROPY, vol. 27, no. 8, 2025, doi:10.3390/e27080820.
- APA
- Ontivero-Ortega, M., Mijatovic, G., Faes, L., Rosas, F. E., Marinazzo, D., & Stramaglia, S. (2025). Localizing synergies of hidden factors in complex systems : resting brain networks and hela geneexpression profile as case studies. ENTROPY, 27(8). https://doi.org/10.3390/e27080820
- Chicago author-date
- Ontivero-Ortega, Marlis, Gorana Mijatovic, Luca Faes, Fernando E. Rosas, Daniele Marinazzo, and Sebastiano Stramaglia. 2025. “Localizing Synergies of Hidden Factors in Complex Systems : Resting Brain Networks and Hela Geneexpression Profile as Case Studies.” ENTROPY 27 (8). https://doi.org/10.3390/e27080820.
- Chicago author-date (all authors)
- Ontivero-Ortega, Marlis, Gorana Mijatovic, Luca Faes, Fernando E. Rosas, Daniele Marinazzo, and Sebastiano Stramaglia. 2025. “Localizing Synergies of Hidden Factors in Complex Systems : Resting Brain Networks and Hela Geneexpression Profile as Case Studies.” ENTROPY 27 (8). doi:10.3390/e27080820.
- Vancouver
- 1.Ontivero-Ortega M, Mijatovic G, Faes L, Rosas FE, Marinazzo D, Stramaglia S. Localizing synergies of hidden factors in complex systems : resting brain networks and hela geneexpression profile as case studies. ENTROPY. 2025;27(8).
- IEEE
- [1]M. Ontivero-Ortega, G. Mijatovic, L. Faes, F. E. Rosas, D. Marinazzo, and S. Stramaglia, “Localizing synergies of hidden factors in complex systems : resting brain networks and hela geneexpression profile as case studies,” ENTROPY, vol. 27, no. 8, 2025.
@article{01K22P2Y0CXCVWSH06PRQPBCD9,
abstract = {{Factor analysis is a well-known statistical method to describe the variability of observed variables in terms of a smaller number of unobserved latent variables called factors. Even though latent factors are conceptually independent of each other, their influence on the observed variables is often joint and synergistic. We propose to quantify the synergy of the joint influence of factors on the observed variables using O-information, a recently introduced metric to assess high-order dependencies in complex systems; in the proposed framework, latent factors and observed variables are jointly analyzed in terms of their joint informational character. Two case studies are reported: analyzing resting fMRI data, we find that DMN and FP networks show the highest synergy, consistent with their crucial role in higher cognitive functions; concerning HeLa cells, we find that the most synergistic gene is STK-12 (AURKB), suggesting that this gene is involved in controlling the HeLa cell cycle. We believe that our approach, representing a bridge between factor analysis and the field of high-order interactions, will find wide application across several domains.}},
articleno = {{820}},
author = {{Ontivero-Ortega, Marlis and Mijatovic, Gorana and Faes, Luca and Rosas, Fernando E. and Marinazzo, Daniele and Stramaglia, Sebastiano}},
issn = {{1099-4300}},
journal = {{ENTROPY}},
keywords = {{information theory,factor analysis,latent factors,high order interactions,synergy,resting-state fMRI,cell gene expression}},
language = {{eng}},
number = {{8}},
pages = {{8}},
title = {{Localizing synergies of hidden factors in complex systems : resting brain networks and hela geneexpression profile as case studies}},
url = {{http://doi.org/10.3390/e27080820}},
volume = {{27}},
year = {{2025}},
}
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