ArticleProceedings of the ... International Conference on Bioinformatics and Biomedical Technology2025
Time-Varying Gene Regulatory Networks Inference Using KL Divergence from Single Cell Data.
Article in Proceedings of the ... International Conference on Bioinformatics and Biomedical Technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
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Who cites it
1 citing paper in PubMed.
- Reconstructing Dynamic Gene Regulatory Networks Using f-Divergence from Time-Series scRNA-Seq Data.Current issues in molecular biology · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Correct reconstruction of dynamic gene regulatory networks from time-series single-cell RNA sequencing (scRNA-seq) data is essential for understanding biological processes, but remains challenging due to high-dimensionality, sparsity, and temporal heterogeneity. We propose a novel framework that integrates Kullback-Leibler divergence (KL) divergence-based temporal variation measurement with an autoregressive model and different regularization methods to infer time-varying regulatory networks from time-series scRNA-seq data. Partial correlation analysis further refines the sign of interactions (activation or inhibition). Simulation studies on a 10-gene synthetic dataset and a THP-1 monocyte differentiation dataset demonstrate that our approach accurately recovers dynamic network structures and maintains temporal consistency.
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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.