How AI Can Advance Mathematical Biology: Opportunities, Challenges, and Future Directions
Kobra Rabiei, Grzegorz A Rempala, Reinhard Laubenbacher, Wenrui Hao · 2026
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Description
Kobra Rabiei, Grzegorz Rempala, Reinhard Laubenbacher, and Wenrui Hao review where artificial intelligence genuinely helps mathematical biology, and where it strains, in a 2026 review. The field's classic tools are equation-based models that stay readable and grounded. Modern data, from clinic records to multi-layer molecular reads, arrive huge, noisy, and patchy, which makes fitting and checking those models hard. Learning methods enter as partners, not replacements.
The review sorts the partnership by task: prediction, hybrid models that mix equations with learning, inference from data, and control of living systems. Examples span medicine, epidemics, ecology, evolution, and population biology. Four recurring worries get emphasis: whether a model can be interpreted, whether its parts can be pinned down at all, whether it generalizes, and whether its uncertainty is honest. The authors aim to clarify roles so collaborations keep biological credibility.
Catalog record
| CREATOR | Kobra Rabiei, Grzegorz A Rempala, Reinhard Laubenbacher, Wenrui Hao |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 113 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |