A knowledge-guided deep learning framework for quantitative nucleic acid testing
Jiayu Yang, Yulin Huang, Zhuolun Li, Fa Zhang, Dongxu Zhang · 2026
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Description
Yang and coauthors build a smart test to count gene bits in a sample. Such tests screen for tumors, gene illness, and infections. Most tools use light to track a gene growth step. The link between light and the growth step shifts with each test setup.
The team adds deep learning guided by known test rules. Their model learns how light curves map to gene counts. It aims to stay right when gene type, sample type, or tools change. The work seeks counts that labs can trust for care and for outbreak control. More checks will show how wide the method can go.
Catalog record
| CREATOR | Jiayu Yang, Yulin Huang, Zhuolun Li, Fa Zhang, Dongxu Zhang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 42 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |