A Systematic Review: Deep Learning for Analyzing Genomic Data to Discover Evolutionary Patterns
Raha Hassanpour Faramoushjani1, Sanam Ansari2
1Department of Biology, Yazd University, Yazd, Iran, yazd.ac.ir.
Scientifica
|July 24, 2026
Summary
Deep learning in evolutionary genomics is advancing, but applications vary across population genetics, phylogenetics, and sequence representation learning. This review categorizes these uses, highlighting domain-specific strengths and limitations for future research.
Area of Science:
- Evolutionary Genomics
- Computational Biology
- Bioinformatics
Background:
- Genomic datasets are growing, increasing the need for advanced computational methods like deep learning.
- Existing deep learning applications in evolutionary genomics lack a unified framework, hindering clear understanding of their diverse uses.
Purpose of the Study:
- To systematically review deep learning applications in evolutionary genomics from 2016-2025.
- To categorize these applications into distinct domains for clarity.
- To identify domain-specific strengths, limitations, and research gaps.
Main Methods:
- Systematic literature review of deep learning in evolutionary genomics (2016-2025).
- Structured screening and qualitative synthesis of 50 selected studies.
- Categorization of applications into population genetic inference, phylogenetic reconstruction, and sequence representation learning.
Main Results:
- Deep learning is used in simulation-based inference for population genetics.
- Neural networks aid phylogenetic tree and model inference.
- Sequence representation learning extracts features using DNA/protein language models.
- Common limitations include interpretability issues, sensitivity to training assumptions, and computational demands.
Conclusions:
- Deep learning offers flexible genomic data modeling but faces challenges in interpretability and evaluation.
- A domain-aligned framework clarifies the landscape of deep learning in evolutionary genomics.
- This review provides a foundation for future methodological advancements in the field.
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