Author

Alejandro A. Schäffer

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Open access Sep 2026

A Machine Learning Framework Enables Supervised Treatment Response Prediction from Tumor Transcriptomics across Cancer Types.

Robustness analysis revealed that predictive performance plateaued for some therapies with increasing training cohorts but continued to improve for others, suggesting inherent limits of supervised brute-force learning for certain treatments, but additional data and deeper mechanistic modeling may further enhance transc...

L. Pal, E. Gertz, N. Nair et al. · Cancer Research · 0 citations

Open access Jul 2026

Proteomic Profiling Identifies CLDN3 as a Tumor-Selective Therapeutic Target in Small Cell Lung Cancer

An integrated proteogenomic pipeline combining single-cell RNA sequencing, combinatorial optimization, mass spectrometry–based proteomics, and immunohistochemistry is developed to systematically map the SCLC surfaceome of 49 tumors across 25 patients with relapsed SCLC and found claudin-3 (CLDN3) to be a consistently e...

Brett Schroeder, Jaehyun Choi, Alejandro A. Schäffer et al. · bioRxiv · 0 citations