Integrated gene Regulation Interpretation System using single-cell multi-omics data
scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
A novel and robust biclustering algorithm for large-scale RNA-Seq data
An R package for Visualization of Differential Gene Expression
Integrative scRNA Seq Interpretation System for Functional Gene Module analysis
A comprehensive model selection for multi-modal single-cell RNA sequencing data
An integrated toolkit for accurate prediction and analysis of cis-regulatory motifs
A novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data
A bioconductor package for qualitative biclustering analysis of gene co-expression data
Integrated Cell-type-specific Regulon Inference Server from Single-cell RNA-Seq
Integrated and systematic views of regulatory DNA motif identification and analyses
Integrated RNA-Seq interpretation system for gene expression data analysis
An integrated platform for long non-coding RNA identification
A single-cell RNA-Seq database for Alzheimer's Disease
Prediction of Regulatory Motifs from Human ChIP-Sequencing Data using a Deep Learning Framework
Elucidation of Biological Networks Across Complex Diseases Using Single-Cell Omics
Clustering and Classification Methods for Single-cell RNA-sequencing Data
Integrative Methods and Practical Challenges for Single-cell Multi-omics
Interpretation of differential gene expression results of RNA-Seq data: review and integration
Application of information theoretical approaches to assess diversity and similarity in single-cell transcriptomics
Single-Cell Techniques and Deep Learning in Predicting Drug Response
