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  11. Computational Strategies For Cross-species Knowledge Transfer And Translational Biomedicine

Computational strategies for cross-species knowledge transfer and translational biomedicine

Hao Yuan1, Christopher A Mancuso2, Kayla Johnson3

  • 1Genetics and Genome Science Program; Ecology, Evolution, and Behavior Program, Michigan State University.

Arxiv
|August 26, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study reviews computational methods for transferring biological knowledge across species, focusing on transcriptome data and molecular networks. It introduces "agnology" for functional equivalence, aiding disease research and drug discovery.

Area of Science:

  • Comparative genomics
  • Systems biology
  • Bioinformatics

Background:

  • Research organisms are vital for understanding human biology and disease.
  • Evolutionary differences between species limit knowledge transfer.
  • Computational approaches are needed to bridge these gaps.

Purpose of the Study:

  • To review state-of-the-art computational methods for cross-species knowledge transfer.
  • To introduce and define "agnology" for functional molecular equivalence.
  • To highlight challenges and future directions in the field.

Main Methods:

  • Focus on methods using transcriptome data and molecular networks.
  • Categorization of knowledge transfer into four key areas.
  • Review of existing literature on cross-species computational biology.

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Main Results:

  • Identification of methods for transferring disease and gene annotation knowledge.
  • Framework for identifying agnologous (functionally equivalent) molecular components.
  • Approaches for inferring equivalent perturbed genes and identifying agnologous cell types.

Conclusions:

  • Cross-species knowledge transfer is crucial for advancing biological research.
  • Agnology provides a framework for understanding functional conservation.
  • Further research is needed to overcome challenges in computational cross-species analysis.