Deep learning insights into distinct patterns of polygenic adaptation across human populations
Devashish Tripathi, Chandrika Bhattacharyya, Analabha Basu · 2024
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Description
Devashish Tripathi and co-authors build a deep learning tool that spots many-gene adaptation in human genomes, in this 2024 paper. Traits shaped by hundreds of small genetic nudges leave faint traces. Older statistics often miss them or cry wolf under tangled population histories.
Their two-stage tool, RAISING, tunes its network first, then selects features and predicts. On published and fresh simulations it beat three rival methods, with far higher true detection and sixty times less compute than one deep rival. A sliding window version scanned four continental population groups. It flagged many regions under adaptation while staying robust to history and selection quirks.
Catalog record
| CREATOR | Devashish Tripathi, Chandrika Bhattacharyya, Analabha Basu |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 92 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |