A Systematic Review: Deep Learning for Analyzing Genomic Data to Discover Evolutionary Patterns
Raha Hassanpour Faramoushjani, Sanam Ansari · 2026
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Description
Raha Hassanpour Faramoushjani and Sanam Ansari reviewed a decade of work applying deep learning to evolutionary genomics. They sorted a scattered literature that is often treated as one field despite sharp inner contrasts. Fifty studies from 2016 to 2025 passed set screening. The reviewers argue the applications fall into three partly overlapping domains, each with its own logic.
Population genetics leans on simulation-based inference. Phylogenetics uses neural networks to speed tree building, or to mimic it, under set conditions. Representation learning trains DNA and protein language models whose features transfer to other tasks. Across all three, ease with complex data is the shared strength. Recurring weaknesses include hard-to-read models, sensitivity to training guesses, uneven scoring habits, and heavy computing demands. The authors offer their domain map as a scaffold for future method building.
Catalog record
| CREATOR | Raha Hassanpour Faramoushjani, Sanam Ansari |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 92055 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |