Wanwen Zeng

CREATE identifies cell-type-specific cis-regulatory elements using a deep learning model that integrates genomic sequences, chromatin accessibility, and chromatin interactions.

This paper introduces a method to infer gene regulatory networks using scATAC-seq data from gene perturbation experiments, showing improved accuracy and interpretability in GRN reconstruction.

This study introduces epiBrainLLM, a framework using genomic LLMs to improve causal analysis of genotype-brain-clinical pathways in Alzheimer's disease.

EpiGePT is a transformer-based model that predicts epigenomic signals and chromatin contacts by incorporating transcription factor activities and 3D genome interactions, outperforming existing methods in cross-cell-type prediction.

DeepDrug uses graph-based deep learning to predict drug-drug and drug-target interactions, outperforming existing methods and identifying potential SARS-CoV-2 treatments.

This paper introduces scDEC-Hi-C, a deep generative framework for analyzing single cell Hi-C data, showing improved clustering and imputation over existing methods.

The paper introduces DeepExpression, a CNN model that predicts gene expression using promoter sequences and HiChIP enhancer-promoter interactions, outperforming baselines in classification and regression tasks.

The paper introduces coupled nonnegative matrix factorizations to integrate single cell RNA-seq and ATAC-seq data by coupling clustering across samples.