Synthesizing Mechanistic Hypotheses from Single-Cell Omics via Discretized Feature Attribution and Empirical Language Model Grounding
An analytical pipeline employing decision trees to discretize continuous neural network attributions into explicit regulatory thresholds is introduced, establishing an auditable methodology to extract robust experimental hypotheses from high-dimensional single-cell data.