Wenle Ren

MINT infers cell-resolved spatial transcriptomes from paired histology and Slide-tags data, improving alignment and expression recovery in lung adenocarcinoma.

The paper benchmarks 13 models for predicting single-cell perturbation responses across 25 datasets, finding performance varies with perturbation effect size and evaluation metric.

The paper introduces a framework for self-auditing AI in drug discovery, using verifiers to validate predictions and adjust objectives based on experimental results.

This study compares six sequencing-based spatial transcriptomic methods using reference tissues, highlighting molecular diffusion as a key variable affecting resolution and identifying unique attributes of spatial transcriptomic data.