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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Human Virome01:26

Human Virome

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The human body harbors a vast and diverse viral community known as the human virome. The virome includes bacteriophages that infect bacteria, and eukaryotic viruses that infect human cells. Transient dietary and environmental viruses also contribute to this dynamic ecosystem. Estimates suggest the human body may contain on the order of 10¹³ viral particles, though abundance varies widely by body site and detection method.Comprehensive characterization of the virome has become possible...
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Mini-Galaxy: Rethinking Complex Human Diseases Through the Lens of Systems Biology and Multilayered AI Network

Cristina Correia1, Choong Yong Ung1, Zhuofei Zhang1,2

  • 1Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA.

International Journal of Molecular Sciences
|April 14, 2026
PubMed
Summary

We introduce the Mini-Galaxy Model (MGM), an AI framework modeling cells as complex networks. This approach reveals hidden gene behaviors driving diseases, enabling new discoveries in medicine.

Keywords:
artificial intelligencelatent gene propertiesnetworksalient gene propertiessystems biology

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Area of Science:

  • Systems biology
  • Computational biology
  • Genomics

Background:

  • Human diseases result from complex interactions of multiple genes and proteins.
  • Gene behavior encompasses more than variants and expression; coordinated activity across scales generates emergent properties.
  • These emergent behaviors, often latent and difficult to measure, significantly influence health and disease.

Purpose of the Study:

  • To propose and describe the Mini-Galaxy Model (MGM), a novel AI-driven network framework.
  • To outline a strategy for constructing and comparing MGMs across health and disease states.
  • To operationalize MGM as a platform for translational medicine, including target prioritization and editing.

Main Methods:

  • Developed a systems-level AI framework (MGM) modeling cells as 'mini-galaxies'.
  • Each 'mini-galaxy' comprises multilayered biological information, representing different dimensions of gene behavior.
  • The framework enables comparison of MGMs to map etiological relatedness and disease mechanisms.

Main Results:

  • The MGM reframes diseases as emergent behaviors of perturbed biological networks.
  • Demonstrated MGM's capability to yield actionable rules for scientific discovery.
  • The model facilitates streamlined biomarker discovery and target selection.

Conclusions:

  • The Mini-Galaxy Model (MGM) provides a novel perspective on human diseases as emergent network behaviors.
  • MGM serves as a powerful discovery platform for translational medicine.
  • The framework accelerates biomarker discovery, intervention design, and drug repurposing.