X. Steven Chen

Cell-of-origin shapes tumor subtype in BRCA1-associated triple-negative breast cancer

This study identifies two cell-of-origin-driven subtypes of BRCA1-associated TNBC, showing that tumor subtype depends on the cell type initiating the cancer.

Multivariate Random Forests for Cross-Modal Multi-Omics Integration

This paper introduces a random forest-based method for multi-omics integration that separates shared and modality-specific signals, showing improved clustering and biological insights in simulations and real datasets.

From aging to Alzheimer's disease: concordant brain DNA methylation changes in late life

This study identifies DNA methylation changes in aging and Alzheimer's disease using brain and blood data, finding concordant CpG sites associated with AD progression.

An Integrative Multi-Omics Random Forest Framework for Robust Biomarker Discovery

This paper introduces a multivariate random forest framework for multi-omics biomarker discovery, showing it outperforms existing methods in identifying biologically relevant biomarkers and pathways.

Integrating Tumor and Organoid DNA Methylation Profiles Reveals Robust Predictors of Chemotherapy Response in Rectal Cancer

This study integrates DNA methylation profiles from tumors and organoids to identify CpGs predictive of chemotherapy response in rectal cancer, finding a methylation risk score significantly predicts progression-free survival.

Patient-Derived Xenograft-Guided Prediction of Chemotherapy Response in Triple-Negative Breast Cancer

This paper develops chemotherapy-specific predictors for triple-negative breast cancer using patient-derived xenografts and multi-omic data, achieving high AUROC scores for response prediction.

An Automatized Workflow to Study Mechanistic Indicators for Driver Gene Prediction with Moonlight

The paper introduces Moonlight2, an updated framework that uses mutation-based mechanistic indicators to identify driver genes in cancer, applied to Basal-like breast cancer.

MethReg: estimating the regulatory potential of DNA methylation in gene transcription

MethReg identifies CpG sites with high regulatory potential by integrating DNA methylation, gene expression, and TF binding data to uncover TF-target gene associations in specific methylation contexts.