首页/数据集/Few-Shot Meta-Learning Applied to Whole Brain Activity Maps Improves Systems Neuropharmacology and Drug Discovery

Few-Shot Meta-Learning Applied to Whole Brain Activity Maps Improves Systems Neuropharmacology and Drug Discovery

NIAID Data Ecosystem2026-05-02 收录

神经药理学建模

少样本学习应用

数据链接：

https://zenodo.org/record/13362829 数据链接链接失效反馈

官方服务：

资源简介：

Data and Code are all included

应用场景：

创建时间：

2024-08-22

神经药理学建模

海马体体积预测

Predictors: serum BDNF after drug interaction, Corrected left hippocampus before. R Square = 0.995. Regression coefficients (b), standard error of b (SEb), Beta and t statistic with the corresponding

Figshare2015-12-02 更新00

Reproducibility Package for a Hybrid Mechanistic–Phenomenological Model Linking BoNT/A Molecular Persistence to Functional Recovery at the Neuromuscular Junction

神经药理学建模

计算神经科学

This record contains the reproducibility package for the study “A Hybrid Mechanistic–Phenomenological Model Linking BoNT/A Molecular Persistence to Functional Recovery at the Neuromuscular Junction.”

Zenodo2026-05-30 更新00

Reproducibility Package for a Hybrid Mechanistic–Phenomenological Model Linking BoNT/A Molecular Persistence to Functional Recovery at the Neuromuscular Junction

神经药理学建模

神经肌肉接头功能模拟

Zenodo2026-05-30 更新00