arXiv：cs.LG（机器学习，全量分类）· Jung Min Kang·· 14 小时前AI 评分33
反向 IRT：面向碎片化癌症药物反应矩阵的稀疏鲁棒排序方法
Reverse Item Response Theory for Sparsity-Robust Ranking in Fragmented Cancer Drug-Response Matrices
AI 导读
研究提出反向 Item Response Theory（IRT），将癌症类型视为具有耐药能力的潜在"被试"、药物视为具有逃逸难度的"项目"，在 GDSC2 数据库 242,036 条药物敏感性测量上估计癌症类型层面的体外耐药性与药物层面的广谱活性。
正文
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-level broad activity on a shared latent scale. Validation across four missingness regimes demonstrates that reverse IRT better recovers the full-data latent ranking than simple averaging, with advantages of Delta-rho = +0.089 to +0.095 at 60% missingness under MCAR, cancer-biased, and drug-biased sparsity. Held-out prediction confirms IRT achieves the best Brier score among five evaluated methods. Bootstrap confidence intervals show 19 of 28 cancer types have stable resistant/sensitive classifications. Cross-platform PRISM replication shows 82% directional agreement but weak rank-order correlation (rho = 0.25), indicating the contribution is methodological robustness under fragmented evaluation, not a universal clinical resistance leaderboard.
| Comments: | 8 pages, 4 figures, 3 tables |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.00002 [cs.LG] |
| (or arXiv:2610.00002v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00002 arXiv-issued DOI via DataCite |
Submission history
From: Jung Min Kang [view email]
[v1]
Sat, 16 May 2026 17:28:31 UTC (63 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org