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Computational strategies for cross-species knowledge transfer.

Hao Yuan^1,2, Christopher A Mancuso³, Kayla Johnson⁴

¹Genetics and Genome Sciences Program, Michigan State University, East Lansing, MI, USA.

|December 13, 2025

Summary

Cross-species knowledge transfer using computational methods helps overcome evolutionary divergence between humans and research organisms. This review highlights techniques for transferring biological insights, disease information, and identifying functional equivalencies.

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Area of Science:

Computational Biology
Genomics
Systems Biology

Background:

Research organisms are vital for understanding human biology and disease.
Evolutionary differences between species limit the direct application of findings.
Computational methods are needed to bridge this knowledge gap.

Purpose of the Study:

To review state-of-the-art computational methods for cross-species knowledge transfer.
To focus on methods utilizing transcriptome data and molecular networks.
To address key areas including disease annotation, functional equivalence, and cell type identification.

Main Methods:

Review of computational approaches for cross-species knowledge transfer.
Focus on transcriptome data and molecular network analysis.
Exploration of methods for identifying equivalent genes, pathways, and cell types.

Main Results:

Identification of methods for transferring disease and gene annotation knowledge.
Strategies for pinpointing functionally equivalent molecular components across species.
Frameworks for inferring equivalent perturbed genes/gene sets and cell types.

Conclusions:

Cross-species knowledge transfer is crucial for advancing biological research.
The concept of 'agnology' (functional equivalence regardless of origin) is proposed.
Future challenges include developing robust integrative models for unresolved evolutionary origins.