Backward Bayesian Outcome Weighted Learning

A central objective of precision medicine is learning optimal dynamic treatment regimes (DTRs) from data. Classification-based methods, like outcome weighted learning (OWL) for single-stage and backward OWL (BOWL) for multi-stage problems, leverage machine learning to directly learn optimal DTRs. However, these methods lack a natural way to quantify uncertainty in treatment decisions at the individual level. In this paper, we extend Bayesian OWL, a Bayesian reformulation of OWL, to the multi-stage setting. We call this method backward Bayesian outcome weighted learning (BBOWL). Like BOWL, our method directly learns an optimal DTR via backward induction, and unlike existing methods, our approach propagates uncertainty backward through the DTR learning process and provides uncertainty quantification of individualized treatment recommendations. We present a theoretical justification of BBOWL and verify its performance via a simulation study.

SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing

Identifying therapeutic target genes from single-cell RNA sequencing (scRNA-seq) data remains a fundamental challenge in translational biology. Unlike bulk assays, scRNA-seq captures heterogeneous cellular states and rare subpopulations, but this same heterogeneity makes target discovery highly sensitive to analytical choices throughout the pipeline, including preprocessing, cell population selection, differential expression analysis, and downstream biological interpretation. As a result, existing workflows and general-purpose analysis agents often produce unstable or difficult-to-interpret target hypotheses, limiting their reliability for disease-focused discovery. We present SCTA (Single-Cell Target Agent), a decision-centric agentic framework for stable and interpretable target gene discovery from scRNA-seq data. Rather than treating analysis as a single general-purpose reasoning task, SCTA decomposes target discovery into specialized agents aligned with key decision points in the single-cell pipeline and constrains downstream reasoning with structured biological evidence. In a representative ablation study on hereditary chronic pancreatitis, we demonstrate that SCTA's full evidence integration yields the most stable target selection across independent runs among the tested configurations, while recovering biologically coherent, disease-relevant mechanisms validated in prior studies. These results suggest that decision-aware agent orchestration tailored to the structure of single-cell analysis can improve the robustness, interpretability, and practical utility of target discovery in precision medicine.