反向IRT用于碎片化癌症药物反应矩阵的稀疏稳健排序
原标题：Reverse Item Response Theory for Sparsity-Robust Ranking in Fragmented Cancer Drug-Response Matrices
AI 摘要
该研究提出反向项目反应理论（reverse IRT），将癌症类型视为具有耐药能力的潜在主体、药物视为具有规避难度的项目，在共享潜在尺度上联合估计癌症类型体外耐药性与药物广谱活性。基于GDSC2数据库242,036条药物敏感性测量数据，在四种缺失机制下验证表明，反向IRT在60%缺失率时比简单平均更能恢复完整数据的潜在排序，并在五种方法中取得最佳Brier分数。PRISM跨平台复现显示82%方向一致性但排序相关性较弱，作者强调贡献在于碎片化评估下的方法学稳健性，而非通用临床耐药排行榜。
正文节选
Reverse Item Response Theory for Sparsity-Robust Ranking in Fragmented Cancer Drug-Response Matrices Abstract We introduce reverse Item Response Theory (IRT) to pharmacogenomic drug-response analysis by treating cancer types as latent “subjects” with resistance ability and drugs as “items” with evasion difficulty. Applied to 242,036 drug sensitivity measurements from the Genomics of Drug Sensitivity in Cancer (GDSC2) database, the model estimates cancer-type-level in-vitro resistance and drug-le