基于深度学习的血管性痴呆小鼠模型Morris水迷宫测试结果预测

资源简介

基于深度学习的血管性痴呆小鼠模型Morris水迷宫测试结果预测

【数据集背景】

本数据集源站标题为《Predicting outcome of Morris water maze test in vascular dementia mouse model with deep learning》。配套论文《Predicting outcome of Morris water maze test in vascular dementia mouse model with deep learning》发表于 PLOS ONE（2018），DOI：10.1371/journal.pone.0191708。源站首次发布：2018-02-07。

【数据内容】

源站原始描述：The Morris water maze test (MWM) is one of the most popular and established behavioral tests to evaluate rodents’ spatial learning ability. The conventional training period is around 5 days, but there is no clear evidence or guidelines about the appropriate duration. In many cases, the final outcome of the MWM seems predicable from previous data and their trend. So, we assumed that if we can predict the final result with high accuracy, the experimental period could be shortened and the burden on…

【数据结构与技术规格】

文件数 6 个，合计 640.94 KB。S1 Table.pdf：application/pdf，103.26 KB。S2 Table.pdf：application/pdf，103.31 KB。S3 Table.pdf：application/pdf，140.87 KB。数据集 DOI：10.1371/journal.pone.0191708。

【主题与分类】

源站学科分类：Medicine、Neuroscience、Physiology、Biological Sciences not elsewhere classified、Science Policy。主题标签：WT、WT-sham、WT-BCAS、Morris water maze test、test data、MWM、ANN system、MLP、tester、4 days。

【适用方向】

源站学科分类标注为 Medicine、Neuroscience、Physiology、Biological Sciences not elsewhere classified、Science Policy。

【获取与许可】

源站页面：https://plos.figshare.com/articles/dataset/Predicting_outcome_of_Morris_water_maze_test_in_vascular_dementia_mouse_model_with_deep_learning/5865258

使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。

规范引用：Higaki, Akinori; Mogi, Masaki; Iwanami, Jun; Min, Li-Juan; Bai, Hui-Yu; Shan, Bao-Shuai; et al. (2018). Predicting outcome of Morris water maze test in vascular dementia mouse model with deep learning. PLOS ONE. Dataset

【使用注意】

· 源站最后修改于 2023-05-30，引用时请注意版本时效。