A versatile attention-based neural network for chemical perturbation analysis and its potential to aid surgical treatment: an experimental study
Zheqi Fan, Houming Zhao, Jingcheng Zhou, Dingchang Li, Yunlong Fan, Yiming Bi, Shuaifei Ji · 2024
Read A versatile attention-based neural network for chemical perturbation analysis and its potential to aid surgical treatment: an experimental study by Zheqi Fan, Houming Zhao, Jingcheng Zhou, Dingchang Li, Yunlong Fan, Yiming Bi, Shuaifei Ji (2024) 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-11634177.txt.
Description
Zheqi Fan and co-authors build an attention based network that predicts how chemicals shift gene activity, in a 2024 paper. Finding drugs that help around surgery could cut complications and speed recovery. Past predictors demand huge datasets and give little insight into why a call was made.
Their network joins graph methods with a transformer and reads both drug structure and gene profiles. It reached top scores with under four hundred training cases, a tenth of what rivals used. By weighing chemical elements and attention weights, it flags which building blocks in a molecule drive effects on a cell line. That clue can steer the hunt for surgical support drugs.
Catalog record
| CREATOR | Zheqi Fan, Houming Zhao, Jingcheng Zhou, Dingchang Li, Yunlong Fan, Yiming Bi, Shuaifei Ji |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 45 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |