=========================
Cross-Study Analysis
=========================


Cross-Study Analysis (CSA) looks at the ability of models to generalize across different datasets. For each model, the CSA workflow generates an N-by-N table of prediction performance scores (N = number of datasets). Scores on the diagonal indicate performance when trained and tested on different splits of the same dataset, whereas off-diagonal scores indicate performance when trained and tested on different datasets


Workflow
----------


Metrics
---------


References
------------
`1. <https://aacrjournals.org/cancerres/article/83/7_Supplement/5380/720598>`_ A. Partin et al. "Systematic evaluation and comparison of drug response prediction models: a case study of prediction generalization across cell lines datasets", AACR, 2023
`2. <https://academic.oup.com/bib/article/23/1/bbab356/6370300>`_ F. Xia et al. "A cross-study analysis of drug response prediction in cancer cell lines", Briefings in Bioinformatics, 2022