Accurate identification of broadly neutralizing antibodies against dengue virus based on deep stacking strategy with multi-perspective features
Saeed Ahmed, Nalini Schaduangrat, Chonlatip Pipattanaboon, Watshara Shoombuatong · 2025
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Description
Antibodies that block all four dengue virus types are prized for drug design, but finding them in the lab is slow and costly. Ahmed and co-authors present a 2025 computer method that flags likely candidates from sequence data alone. The method stacks many learning models into one layered system.
Several coding schemes turn antibody sequences into numbers that capture key traits of the binding region. The stacked models then vote on each candidate. High accuracy in tests suggests the tool can cut the lab list to the most promising entries. Faster screening could speed work on dengue treatments, a need in many warm countries where the fever spreads by mosquito bites.
Catalog record
| CREATOR | Saeed Ahmed, Nalini Schaduangrat, Chonlatip Pipattanaboon, Watshara Shoombuatong |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 41 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |