ArticleBMC systems biology2018
SCNS: a graphical tool for reconstructing executable regulatory networks from single-cell genomic data.
Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses that pooled it.
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Who cites it
47 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- What are the applications of single-cell RNA sequencing in cancer research: a systematic review.Journal of experimental & clinical cancer research : CR · 2021Pooled it
- CoReGRN: a context-aware post-processing framework for gene regulatory network inference revealing regulatory signatures in HIV-Leishmaniasis.BMC genomics · 2026Article
- Multi-omic network inference from time-series data.NPJ systems biology and applications · 2025Article
- Automated model refinement using perturbation-observation pairs.NPJ systems biology and applications · 2025Article
- Reconstructing Dynamic Gene Regulatory Networks Using f-Divergence from Time-Series scRNA-Seq Data.Current issues in molecular biology · 2025Article
- scMINER: a mutual information-based framework for clustering and hidden driver inference from single-cell transcriptomics data.Nature communications · 2025Article
- Gene regulatory network inference during cell fate decisions by perturbation strategies.NPJ systems biology and applications · 2025Article
- Attractor Landscape Analysis Reveals a Reversion Switch in the Transition of Colorectal Tumorigenesis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Advances in modeling cellular state dynamics: integrating omics data and predictive techniques.Animal cells and systems · 2025Review
- From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling.iScience · 2024Review
- Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery.Nature neuroscience · 2024Review
- CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data.Interdisciplinary sciences, computational life sciences · 2024Article
- scMGATGRN: a multiview graph attention network-based method for inferring gene regulatory networks from single-cell transcriptomic data.Briefings in bioinformatics · 2024Article
- Inference of single-cell network using mutual information for scRNA-seq data analysis.BMC bioinformatics · 2024Article
- Canalizing kernel for cell fate determination.Briefings in bioinformatics · 2024Article
- TopoDoE: a design of experiment strategy for selection and refinement in ensembles of executable gene regulatory networks.BMC bioinformatics · 2024Article
- DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.Briefings in bioinformatics · 2024Article
- Inference of gene regulatory networks based on directed graph convolutional networks.Briefings in bioinformatics · 2024Article
- Decoding the principle of cell-fate determination for its reverse control.NPJ systems biology and applications · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundReconstruction of executable mechanistic models from single-cell gene expression data represents a powerful approach to understanding developmental and disease processes. New ambitious efforts like the Human Cell Atlas will soon lead to an explosion of data with potential for uncovering and understanding the regulatory networks which underlie the behaviour of all human cells. In order to take advantage of this data, however, there is a need for general-purpose, user-friendly and efficient computational tools that can be readily used by biologists who do not have specialist computer science knowledge.
resultsThe Single Cell Network Synthesis toolkit (SCNS) is a general-purpose computational tool for the reconstruction and analysis of executable models from single-cell gene expression data. Through a graphical user interface, SCNS takes single-cell qPCR or RNA-sequencing data taken across a time course, and searches for logical rules that drive transitions from early cell states towards late cell states. Because the resulting reconstructed models are executable, they can be used to make predictions about the effect of specific gene perturbations on the generation of specific lineages.
conclusionsSCNS should be of broad interest to the growing number of researchers working in single-cell genomics and will help further facilitate the generation of valuable mechanistic insights into developmental, homeostatic and disease processes.
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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.