# bulk RNA-seq

Blank Bio has released Ladon, a 3-billion-parameter transcriptomic foundation model pretrained with a self-supervised objective on millions of RNA-seq samples to predict patient prognosis and therapeutic response from pre-treatment expression data. Across six held-out clinical trials in bladder, kidney, lung and liver cancer, Ladon significantly outperformed all baselines for immunotherapy response prediction, including PD-L1 expression; a score built for the POPLAR trial behaved as a predictive rather than merely prognostic biomarker.

Blank Bio has released Ladon, a 3-billion-parameter transcriptomic foundation model pretrained with a self-supervised objective on millions of RNA-seq samples to predict patient prognosis and therapeutic response from pre-treatment expression data. Across six held-out clinical trials in bladder, kidney, lung and liver cancer, Ladon significantly outperformed all baselines for immunotherapy response prediction, including PD-L1 expression; a score built for the POPLAR trial behaved as a predictive rather than merely prognostic biomarker.

In bulk RNA-seq differential expression, general-purpose LLM agents tend to pick DESeq2 or edgeR without checking whether those methods' statistical assumptions fit the data at hand. DE-Rigor-Agent splits the analysis into input standardization, method filtering and method execution, and before running anything it evaluates six common methods against expert-curated method cards and dataset-specific diagnostics, recording why each is kept or excluded. Across 140 simulated datasets its filtering decisions largely matched the incompatibilities built into the simulations and stayed stable across random seeds, while two Codex baselines chose DESeq2 or edgeR for nearly all datasets.

In bulk RNA-seq differential expression, general-purpose LLM agents tend to pick DESeq2 or edgeR without checking whether those methods' statistical assumptions fit the data at hand. DE-Rigor-Agent splits the analysis into input standardization, method filtering and method execution, and before running anything it evaluates six common methods against expert-curated method cards and dataset-specific diagnostics, recording why each is kept or excluded. Across 140 simulated datasets its filtering decisions largely matched the incompatibilities built into the simulations and stayed stable across random seeds, while two Codex baselines chose DESeq2 or edgeR for nearly all datasets.