ArticleInternational journal of molecular sciences2023
MetaSEM: Gene Regulatory Network Inference from Single-Cell RNA Data by Meta-Learning.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- SGMHA: semantic graph reconstruction with multi-head attention for gene regulatory network inference.BMC genomics · 2026Article
- Identifying potential ligand-receptor interactions by integrating LSTM network and the attention mechanism for cell-cell communication prediction.Journal of translational medicine · 2026Article
- Cell line-specific gene network enrichment analysis for interpreting continuous phenotypes.Briefings in bioinformatics · 2026Article
- CaHoT-GRN: context-aware high-order topology learning for robust single-cell gene regulatory network inference.Briefings in bioinformatics · 2026Article
- A DSSM network for inferring and prioritizing cell-type-specific regulons using single-cell RNA-seq data.BMC bioinformatics · 2025Article
- Decoding yeast transcriptional regulation via a data-and mechanism-driven distributed large-scale network model.Synthetic and systems biotechnology · 2025Article
- Structure-enhanced graph meta learning for few-shot gene regulatory network inference.Genome biology · 2025Article
- GAEDGRN: reconstruction of gene regulatory networks based on gravity-inspired graph autoencoders.Briefings in bioinformatics · 2025Article
- SIGRN: Inferring Gene Regulatory Network with Soft Introspective Variational Autoencoders.International journal of molecular sciences · 2024Article
- Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
- GMFGRN: a matrix factorization and graph neural network approach for gene regulatory network inference.Briefings in bioinformatics · 2024Article
- Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction.BMC genomics · 2023Article
- STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Regulators in gene regulatory networks (GRNs) are crucial for identifying cell states. However, GRN inference based on scRNA-seq data has several problems, including high dimensionality and sparsity, and requires more label data. Therefore, we propose a meta-learning GRN inference framework to identify regulatory factors. Specifically, meta-learning solves the parameter optimization problem caused by high-dimensional sparse data features. In addition, a few-shot solution was used to solve the problem of lack of label data. A structural equation model (SEM) was embedded in the model to identify important regulators. We integrated the parameter optimization strategy into the bi-level optimization to extract the feature consistent with GRN reasoning. This unique design makes our model robust to small-scale data. By studying the GRN inference task, we confirmed that the selected regulators were closely related to gene expression specificity. We further analyzed the GRN inferred to find the important regulators in cell type identification. Extensive experimental results showed that our model effectively captured the regulator in single-cell GRN inference. Finally, the visualization results verified the importance of the selected regulators for cell type recognition.
Indexed as
Identifiers
What OpenQuestion holds
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.