ReviewComputational and structural biotechnology journal2020
Integration of single-cell multi-omics for gene regulatory network inference.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 66 citations in OpenAlex.
- PSGRN: Gene regulatory network inference from single-cell perturbational data through self-training with synthetic gold standards.Science advances · 2026Article
- STAN, a computational framework for inferring spatially informed transcription factor activity.Nucleic acids research · 2026Article
- The Need for a Systems Biology Approach in Cancer Explained.International journal of molecular sciences · 2025Review
- A large-scale benchmark for network inference from single-cell perturbation data.Communications biology · 2025Article
- Cell Fate Dynamics Reconstruction Identifies TPT1 and PTPRZ1 Feedback Loops as Master Regulators of Differentiation in Pediatric Glioblastoma-Immune Cell Networks.Interdisciplinary sciences, computational life sciences · 2025Article
- Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.Nucleic acids research · 2025Article
- CORN 2.0 - Condition Orientated Regulatory Networks 2.0.Computational and structural biotechnology journal · 2025Article
- A novel and robust feature selection method with FDR control for omics-wide association analysis.PloS one · 2025Article
- scMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data.BMC bioinformatics · 2024Article
- PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization.Genome biology · 2024Article
- SAILoR: Structure-Aware Inference of Logic Rules.PloS one · 2024Article
- scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets.International journal of molecular sciences · 2023Article
- Stability selection for LASSO with weights based on AUC.Scientific reports · 2023Article
- A novel Boolean network inference strategy to model early hematopoiesis aging.Computational and structural biotechnology journal · 2023Article
- Computational approaches and challenges for identification and annotation of non-coding RNAs using RNA-Seq.Functional & integrative genomics · 2022Review
- Single Cell Transcriptomics to Understand HSC Heterogeneity and Its Evolution upon Aging.Cells · 2022Review
- Biomarker Genes Discovery of Alzheimer's Disease by Multi-Omics-Based Gene Regulatory Network Construction of Microglia.Brain sciences · 2022Article
- Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis.Communications biology · 2022Article
- Uncovering the mesendoderm gene regulatory network through multi-omic data integration.Cell reports · 2022Article
- Exploring long non-coding RNA networks from single cell omics data.Computational and structural biotechnology journal · 2022Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advancement of single-cell sequencing technology in recent years has provided an opportunity to reconstruct gene regulatory networks (GRNs) with the data from thousands of single cells in one sample. This uncovers regulatory interactions in cells and speeds up the discoveries of regulatory mechanisms in diseases and biological processes. Therefore, more methods have been proposed to reconstruct GRNs using single-cell sequencing data. In this review, we introduce technologies for sequencing single-cell genome, transcriptome, and epigenome. At the same time, we present an overview of current GRN reconstruction strategies utilizing different single-cell sequencing data. Bioinformatics tools were grouped by their input data type and mathematical principles for reader's convenience, and the fundamental mathematics inherent in each group will be discussed. Furthermore, the adaptabilities and limitations of these different methods will also be summarized and compared, with the hope to facilitate researchers recognizing the most suitable tools for them.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.