five

Performance in validation on isolates not present in the original dataset.

收藏
Figshare2015-12-02 更新2026-04-29 收录
下载链接:
https://figshare.com/articles/dataset/_Performance_in_validation_on_isolates_not_present_in_the_original_dataset_/665732
下载链接
链接失效反馈
官方服务:
资源简介:
Validation parameters were calculated using different forms of grouping to give an unbiased error estimate. Class wide values are indicated in italic and the global average performance is indicated in bold and italic. For larger groups (RefID, SeqID, Isolatename and per drug) the average value and standard deviation are given. For three drugs (RTV, DLV, DDC) no Virco cut-off was available, here the Stanford cut off was used for both, for SQV no Stanford cut-off was available so the Virco cut-off was used for both. The table shows that our PCM models perform robustly in predicting the Log FC as indicated by the regression validation parameters RMSE and R02. More importantly, the correctly classified percentage is 84% overall.
创建时间:
2015-12-02
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作