Yue You

This study identifies γδ T cell infiltration in SCLC and shows their anti-tumor activity, suggesting they could be therapeutic targets.

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.

RVQ-Alpha adapts large language models for single-cell analysis by integrating hierarchical tokenization and reinforcement learning, achieving performance comparable to task-specific models on a multi-task benchmark.

stPipe provides a flexible R/Bioconductor pipeline for preprocessing spatial transcriptomics data from multiple platforms, enabling standardized analysis and benchmarking.

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.

The paper introduces two methods to handle group heteroscedasticity in pseudo-bulk single-cell RNA-seq data, showing improved error control and power compared to existing methods.

This study benchmarks 9 scRNA-seq preprocessing workflows, finding that while they vary in gene detection and quantification, downstream analysis with effective normalization and clustering methods leads to consistent clustering results.