Factor‐Augmented Neural Conditional Density Estimation with Application to Drug Response Prediction

ABSTRACT

High‐dimensional pharmacogenomic data pair thousands of molecular features with hundreds of cancer cell lines, complicating conditional density estimation. We study factor‐augmented CINDES, which estimates conditional response densities from principal‐component scores through binary classification. For an honest pilot/main‐sample construction, assuming fresh‐score PCA recovery, we derive a risk bound separating conditional‐density learning, PCA score error, and representation bias. This guarantee does not cover the implemented procedure, which reuses observations for PCA and CINDES. In favorable strong‐factor simulations, PCA improved a fixed CINDES pipeline, but PCA‐MDN achieved lower negative log‐likelihood (NLL) and integrated error in the matched comparison. Stress tests showed losses from omitted response‐relevant low‐variance directions and weak factor recovery, with sample‐size‐dependent factor‐count effects. Across all 692 eligible source‐specific genomics of drug sensitivity in cancer (GDSC) units and five splits, mean held‐out NLL was 1.5414 for PCA‐CINDES, versus 1.5661 and 1.5557 for ElasticNet and PCA/Ridge Gaussian models; PCA‐MDN was better at 1.5156. A previously fixed 35‐unit audit favored PCA‐CINDES over Raw‐X CINDES (mean paired NLL difference ) and found lower measured computational cost. The evidence supports PCA compression when retained factors capture response information and are accurately estimated; it does not establish robustness to general nonlinear or nonfactor structures, or CINDES as the preferred second‐stage estimator. The application concerns preclinical prediction rather than clinical utility.