CWAGS: multi-trait genomic selection using channel weighted attention convolutional network
Chunqing Cao, Farhan Bin Mohamed, Mohd Shahrizal Bin Sunar, Vei Siang Chan · 2026
Read CWAGS: multi-trait genomic selection using channel weighted attention convolutional network by Chunqing Cao, Farhan Bin Mohamed, Mohd Shahrizal Bin Sunar, Vei Siang Chan (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13386843.txt.
Description
Chunqing Cao's team offers CWAGS, a neural network for multi-trait genomic selection in crops, in a 2026 paper. Genomic selection speeds crop improvement, but catching complex links among genetic variants remains hard. CWAGS uses channel-weighted focus to reveal trait-specific genetic patterns, depthwise split filters for speed, and DropPath with skip links to aid transfer across diverse genetic backgrounds.
Channel attention weights showed distinct genetic patterns for different traits, backing the model's genetic insight. Across four test sets, CWAGS beat the best rival models by about 1 to 5 percent in average scores. By weighing scores with insight, CWAGS offers a model toolkit for exact genomic selection and faster crop breeding.
Catalog record
| CREATOR | Chunqing Cao, Farhan Bin Mohamed, Mohd Shahrizal Bin Sunar, Vei Siang Chan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 46 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |