Artificial Intelligence in genomics: a comprehensive survey of methods, resources, challenges, and prospects
Md Ishtyaq Mahmud, Tania Banerjee · 2026
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Description
Ishtyaq Mahmud and Tania Banerjee survey how artificial intelligence is reshaping genomics. Machine learning, deep learning, language models, and generative systems now help read disease mechanisms, design treatments, and tailor care to the patient. The review maps these methods onto tasks such as sequencing, spotting variants, reading gene activity, and guiding CRISPR edits.
The authors catalog the main tools, benchmark datasets, and open software that support the field. Problems persist: data quality, hard-to-explain models, ethics, and sheer computing scale. They see promise in merging many kinds of biological data through graph networks and multimodal models, and in newer ideas such as synthetic genomes and digital twins of patients.
Catalog record
| CREATOR | Md Ishtyaq Mahmud, Tania Banerjee |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 55 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |