Prioritizing missense mutations via a deep learning phosphorylation prediction model
Yue Xu, Yuan Zhou, Kun Li, Shiao Zhou, Hai Yang, Dan Zhou · 2025
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Description
Yue Xu and co-authors build a learning model to rank gene changes by their effect on a key protein mark in a 2025 paper. The mark, phosphorylation, adds variety to proteins, and its faults link to complex traits, above all brain disorders. Single letter gene changes near a marked site can shift the mark, but which changes matter was unclear.
Their model was the first trained on a brain specific data set from China. It beat older tools on both that set and public sets. By comparing normal and changed protein strings, they scored each change. High impact changes clustered in gene zones tied to Alzheimer disease and other traits, which helps rank changes for study.
Catalog record
| CREATOR | Yue Xu, Yuan Zhou, Kun Li, Shiao Zhou, Hai Yang, Dan Zhou |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 53 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |