Rui Jiang

EpiZoo: a DNA sequence-aware foundation model for cross-species single-cell epigenomics

EpiZoo is a cross-species single-cell epigenomic foundation model that uses DNA sequence-aware architecture to improve cell type annotation and mutation prioritization across species.

Hi-Cformer enables multi-scale chromatin contact map modeling for single-cell Hi-C data analysis

Hi-Cformer models multi-scale chromatin contact maps from single-cell Hi-C data, improving cell type separation and imputation of 3D genome features.

Cross-modality representation and multi-sample integration of spatially resolved omics data

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.

ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations

ERNIE-RNA is an RNA language model that incorporates base-pairing restrictions to improve structural representation, achieving state-of-the-art performance in RNA structural and functional predictions.

Human-scATAC-Corpus: a comprehensive database of scATAC-seq data

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

CREATE: cell-type-specific cis-regulatory elements identification via discrete embedding

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

INSTINCT: Multi-sample integration of spatial chromatin accessibility sequencing data via stochastic domain translation

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

EpiAgent: Foundation model for single-cell epigenomic data

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

Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model

The paper introduces UniCoord, a joint-VAE model that learns a universal coordinate system for single-cell transcriptomic data to capture heterogeneity and enable data generation and analysis.

Detection of spatial chromatin accessibility patterns with inter-cellular correlations

This paper introduces Descartes, a model for identifying spatially variable chromatin accessibility peaks using inter-cellular correlations, showing improved performance and efficiency over existing methods.

collectNET: a web server for integrated inference of cell-cell communication network

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

Cofea: correlation-based feature selection for single-cell chromatin accessibility data

Cofea selects biologically relevant features from single-cell chromatin accessibility data by focusing on correlations between accessible regions, improving downstream analysis.

PAST: latent feature extraction with a Prior-based self-Attention framework for Spatial Transcriptomics

PAST characterizes spatial domains in spatial transcriptomics data using a variational graph convolutional auto-encoder with prior-based self-attention, improving downstream analyses.

scEpiTools: a database to comprehensively interrogate analytic tools for single-cell epigenomic data

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

simCAS: an embedding-based method for simulating single-cell chromatin accessibility sequencing data

simCAS generates high-fidelity scCAS data using embeddings, enabling benchmarking of computational methods for cell clustering, trajectory inference, and cis-regulatory interaction analysis.

EpiGePT: a Pretrained Transformer model for epigenomics

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.

A generic reference defined by consensus peaks for scATAC-seq data analysis

The paper constructs a generic chromatin accessibility reference (cPeaks) from bulk ATAC-seq data to improve scATAC-seq analysis and cell type identification.

DeepDrug: A general graph-based deep learning framework for drug-drug interactions and drug-target interactions 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.

Deep generative modeling and clustering of single cell Hi-C data

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.

hECA: the cell-centric assembly of a cell atlas

The paper introduces hECA, a cell-centric human Ensemble Cell Atlas, which integrates scRNA-seq data from multiple studies to enable targeted data retrieval, multi-view presentations, and customizable reference creation for cell type annotations.

Incorporating Gene Expression in Genome-wide Prediction of Chromatin Accessibility via Deep Learning

DeepCAGE predicts chromatin accessible regions using deep learning, integrating sequence data and transcription factor activity, and identifies disease-associated variants.

OpenAnnotate: a web server to annotate the chromatin accessibility of genomic regions

OpenAnnotate provides a web server for efficiently annotating chromatin accessibility of genomic regions across biosamples, tissues, and systems.

A reference-guided approach for epigenetic characterization of single cells

This paper introduces RA3, a reference-guided method for analyzing single-cell chromatin accessibility data, showing improved performance across multiple datasets.

DeepCAPE: a deep convolutional neural network for the accurate prediction of enhancers

DeepCAPE predicts enhancers using DNA sequences and DNase-seq data, outperforming existing methods and enabling cell line-specific identification.

NeuronMotif: Deciphering transcriptional cis-regulatory codes from deep neural networks

NeuronMotif decodes cis-regulatory motifs from DCNNs by analyzing neuron activations, revealing motif combinations and improving model performance.

Determine independent gut microbiota-diseases association by eliminating the effects of human lifestyle factors

The paper uses machine learning to assess gut microbiota-disease associations while accounting for human lifestyle factors, finding independent associations with IBD and unhealthy status but not others.

DeepCDR: a hybrid graph convolutional network for predicting cancer drug response

DeepCDR predicts cancer drug response by integrating multi-omics data and drug chemical structures using a hybrid graph convolutional network, outperforming existing methods.

EnClaSC: A novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomes

EnClaSC is an ensemble method for cell-type classification in scRNA-seq data, showing high accuracy and robustness across datasets and species.

Simultaneous deep generative modeling and clustering of single cell genomic data

scDEC uses deep generative networks to simultaneously model and cluster scATAC-seq data, showing better performance than existing tools and enabling trajectory inference and cell type discovery.

Integrating distal and proximal information to predict gene expression via a densely con-nected convolutional neural network

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.