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Related Experiment Video

Updated: May 2, 2026

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Predicting emergent phenotypes from single cell populations using CELLECTION.

Hongru Hu1,1,2, Siddhant Sanghi1,1, Gerald Quon1,2

  • 1Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, 95616, USA.

Biorxiv : the Preprint Server for Biology
|September 15, 2025
PubMed
Summary
This summary is machine-generated.

We developed CELLECTION, a deep learning framework to identify cellular and genetic signatures. This approach links subgroups of biological instances to emergent phenotypes for disease, development, and evolution research.

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Area of Science:

  • Computational Biology
  • Genomics
  • Developmental Biology

Background:

  • Biological systems display emergent phenotypes from collective component behavior.
  • Understanding these emergent properties is key in fields like genetics and developmental biology.

Purpose of the Study:

  • To introduce CELLECTION, a deep learning framework for associating instance subgroups with emergent phenotypes.
  • To demonstrate CELLECTION's utility in diverse biological research areas.

Main Methods:

  • Developed a deep learning framework named CELLECTION.
  • Applied CELLECTION to heterogeneous tasks including disease classification and evolutionary analysis.

Main Results:

  • CELLECTION enables interpretable predictions for complex biological tasks.
  • Successfully applied to identify disease-associated cell subtypes and align developmental stages.
  • Used to predict avian relative hand-wing indices, showcasing evolutionary insights.

Conclusions:

  • CELLECTION offers a scalable and flexible method for uncovering biological insights.
  • Identifies key cellular or genetic signatures underlying complex traits.
  • Facilitates research in development, disease, and evolutionary biology.