Peng Jiang

AI-Driven Spatial Transcriptomics Unlocks Large-Scale Breast Cancer Biomarker Discovery from Histopathology

Path2Space predicts spatial gene expression and tumor microenvironment from histopathology slides, identifying new breast cancer subgroups and improving biomarker prediction.

Deep learning inference of cell type-specific gene expression from breast tumor histopathology

SLIDE-EX predicts cell type-specific gene expression from breast cancer histopathology images, outperforming bulk RNA-seq deconvolution and slide-based models in chemotherapy response prediction.

Web engine for tumor pathology image retrievals on massive scales

HERE is an image retrieval engine for H&E tumor pathology images that uses AI encoding and indexing to efficiently find similar images across large datasets.

Predicting tumor immune microenvironment and checkpoint therapy response of head & neck cancer patients from blood immune single-cell transcriptomics

The paper infers tumor immune status and predicts immunotherapy response from blood scRNA-Seq data in HNSCC patients.

Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression

This study identifies a decoupling signature that reduces the correlation between cytotoxic and exhausted T cell activities, improving the prediction of immunotherapy response in melanoma.

Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors

This paper introduces PERCEPTION, a framework using single-cell transcriptomics to predict cancer treatment response and resistance, outperforming bulk expression-based methods in clinical trials.

An integrative ENCODE resource for cancer genomics

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.

Integrative analysis and refined design of CRISPR knockout screens

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.

Exploring functional variation affecting ceRNA regulation in humans

The paper identifies genetic variations affecting ceRNA regulation by analyzing RNA-seq data and GWAS results, revealing new regulatory mechanisms and disease associations.