ArticleNPJ systems biology and applications2021
Constructing gene regulatory networks using epigenetic data.
Article in NPJ systems biology and applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- BLOBFISH: bipartite limited subnetworks from multiple observations using breadth-first search with constrained hops.Bioinformatics (Oxford, England) · 2026Article
- Bacterial gene regulatory neural network as a biocomputing library of mathematical solvers.NPJ systems biology and applications · 2026Article
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- A systems approach for elucidating the role of the microbiome in epigenetic cellular memory.mSystems · 2025Review
- Harnessing the analog computing power of regulatory networks with the Regulatory Network Machine.iScience · 2025Article
- Gene regulation technologies for gene and cell therapy.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Interpretable AI for inference of causal molecular relationships from omics data.Science advances · 2025Article
- Integrated analysis of chromatin and transcriptomic profiling of the striatum after cerebral hypoperfusion in mice.BMC genomics · 2025Article
- In silico screening system based on a transcription factors regulatory network only using transcriptomic data.PloS one · 2025Article
- Article
- Integration of multi-omics summary data reveals the role of N6-methyladenosine in neuropsychiatric disorders.Molecular psychiatry · 2024Article
- A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.Briefings in bioinformatics · 2024Review
- Mechanisms and technologies in cancer epigenetics.Frontiers in oncology · 2024Review
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Recent advances in exploring transcriptional regulatory landscape of crops.Frontiers in plant science · 2024Review
- Methods for multi-omic data integration in cancer research.Frontiers in genetics · 2024Review
- Single-sample network modeling on omics data.BMC biology · 2023Article
- Article
- Gene regulatory network inference in the era of single-cell multi-omics.Nature reviews. Genetics · 2023Review
- The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks.Genome biology · 2023Article
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4 authors.
Funding
Abstract
The biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell's epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER's predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms.
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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.