Abstract
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Wuchao Liu, Peng Han, Wengen Li, Jihong Guan, Shuigeng Zhou
Abstract
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10.1186/1471-2164-10-403 · doi-reference
Identification and characterization of a novel monocyte subpopulation in human peripheral blood
10.1182/blood.v74.7.2527.2527 · doi-reference
CD14: biology and role in the pathogenesis of disease
10.1016/j.cytogfr.2019.06.003 · doi-reference
Definition of human blood monocytes
10.1002/jlb.67.5.603 · doi-reference
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics
10.1186/s12864-018-4772-0 · doi-reference
SciPy 1.0: fundamental algorithms for scientific computing in python
10.1038/s41592-019-0686-2 · doi-reference
GSEApy: a comprehensive package for performing gene set enrichment analysis in python
10.1093/bioinformatics/btac757 · doi-reference
SCANPY: large-scale single-cell gene expression data analysis
10.1186/s13059-017-1382-0 · doi-reference
High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell
10.1038/s41587-019-0290-0 · doi-reference
Chromatin potential identified by shared single-cell profiling of RNA and chromatin
10.1016/j.cell.2020.09.056 · doi-reference
Negative can be positive: a stable and noise-resistant complementary contrastive learning for cross-modal matching
10.1016/j.inffus.2026.104156 · doi-reference
Global and cross-modal feature aggregation for multi-omics data classification and application on drug response prediction
10.1016/j.inffus.2023.102077 · doi-reference
Sarcasm driven by sentiment: a sentiment-aware hierarchical fusion network for multimodal sarcasm detection
10.1016/j.inffus.2024.102353 · doi-reference
scSPAF: cell similarity purified adaptive fusion network for single-cell multi-omics clustering
10.1109/tcbbio.2025.3608251 · doi-reference
scMCs: a framework for single-cell multi-omics data integration and multiple clusterings
10.1093/bioinformatics/btad133 · doi-reference
Multi-view adaptive fusion network for spatially resolved transcriptomics data clustering
10.1109/tkde.2024.3450333 · doi-reference
SpaFusion: a multi-level fusion model for clustering spatial multi-omics data
10.1016/j.inffus.2025.103372 · doi-reference
Multi-kernel subspace stable clustering with exact rank constraints
10.1016/j.inffus.2024.102488 · doi-reference
scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks
10.1038/s41592-022-01562-8 · doi-reference
scGPT: toward building a foundation model for single-cell multi-omics using generative AI
10.1038/s41592-024-02201-0 · doi-reference
Gene2vec: distributed representation of genes based on co-expression
10.1186/s12864-018-5370-x · doi-reference
Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network
10.1093/bib/bbac018 · doi-reference
Deep generative modeling for single-cell transcriptomics
10.1038/s41592-018-0229-2 · doi-reference
A general and flexible method for signal extraction from single-cell RNA-seq data
10.1038/s41467-017-02554-5 · doi-reference
Spatial reconstruction of single-cell gene expression data
10.1038/nbt.3192 · doi-reference
Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data
10.1093/bioinformatics/btab403 · doi-reference
A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data
10.1093/bioinformatics/btad005 · doi-reference
scMLC: an accurate and robust multiplex community detection method for single-cell multi-omics data
10.1093/bib/bbae101 · doi-reference
Manifold alignment for heterogeneous single-cell multi-omics data integration using pamona
10.1093/bioinformatics/btab594 · doi-reference
JSNMF enables effective and accurate integrative analysis of single-cell multiomics data
10.1093/bib/bbac105 · doi-reference
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
10.1186/s13059-020-02015-1 · doi-reference