X. Shirley Liu

This study finds that higher pretreatment γδ T-cell abundance is linked to better outcomes in cancer patients receiving immune checkpoint blockade and improved survival across multiple tumor types.

The paper introduces scDist, a tool for detecting perturbed cell types in single-cell RNA-seq data, showing it outperforms existing methods in identifying transcriptomic changes in disease contexts.

The paper applies transfer learning to predict network biology features across different biological contexts.

The paper develops a machine learning model to predict protein degradability for targeted protein degradation, identifying key features and extending predictions to the proteome.

MIRA jointly models gene expression and chromatin accessibility in single cells to infer regulatory circuits and lineage trees during development.

This study shows MYC overexpression disrupts AR transcriptional pause release, promoting aggressive prostate cancer progression.

Chromap provides a fast and accurate method for aligning and preprocessing chromatin profiles, outperforming existing tools in speed while maintaining alignment accuracy.

CHIPS is a Snakemake pipeline for processing chromatin profiling data, providing quality control metrics and reproducible analysis workflows for ChIP-seq, ATAC-seq, and DNase-seq experiments.

The paper develops a custom ENCODE annotation for cancer genomics, highlighting TF-RBP interactions and network rewiring during oncogenesis, and validates findings using experimental assays.

The paper identifies two classes of transcription factors based on their regulatory range and shows that long-range TFs' activity depends on chromatin state.

DeepBCR predicts cancer type and binding affinity of BCRs using deep learning, validated with anti-PD1 drug affinities.

The paper introduces MAGeCK-NEST, a method to improve CRISPR screen analysis by integrating PPI data and refining sgRNA design, leading to better hit calling and library design.

MACS calls ChIP-Seq peaks by estimating tag shift from Watson/Crick strand offsets and testing enrichment against a locally adaptive Poisson background, improving specificity and binding-site resolution.