Zijing Gao

scMTG reconstructs single-cell temporal dynamics from unpaired time-series data by learning cell representations and transitions, outperforming existing methods in interpolation and transition matrix inference.

RegFM improves gene regulation modeling by integrating cis-regulatory elements and trans-acting regulators in human cells, outperforming existing methods in multiple tasks.

This paper introduces PRESENT, a contrastive learning framework for integrating spatially resolved multi-omics data across samples and modalities, showing improved performance in detecting spatially functional domains and biological regulatory mechanisms.

HitAnno annotates cell types in atlas-level scATAC-seq data using a hierarchical language model, accurately handling major and rare cell types across multiple settings.

The paper introduces Human-scATAC-Corpus, a database of human scATAC-seq data, and presents a foundation model for epigenomic analysis.

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

INSTINCT integrates multi-sample spatial chromatin accessibility sequencing data by removing batch effects and noise, enabling comprehensive analysis of epigenomic landscapes.

EpiAgent is a foundation model for single-cell epigenomic data that excels in feature extraction, cell annotation, and data imputation.

collectNET infers cell-cell communication networks using three methods and provides an online tool with visualization and a human communication atlas.

scEpiTools is a database that organizes and recommends tools for analyzing single-cell epigenomic data, including an ensemble method for chromatin accessibility analysis.

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.