ArticleBioinformatics research and applications : ... international symposium, ISBRA ... proceedings. ISBRA (Conference)2025
Gene Regulatory Network Inference from Pseudotime-Ordered scRNA-seq Data via Time-Lagged Divergence Measures.
Article in Bioinformatics research and applications : ... international symposium, ISBRA ... proceedings. ISBRA (Conference), 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
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
1 citing paper in PubMed.
- Pseudotime-conditioned diffusion models for imputing time-series single-cell data.Bioinformatics advances · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Inferring cell type-specific gene regulatory networks (GRNs) from time-series single-cell RNA sequencing (scRNA-seq) data is challenging due to sparse temporal resolution, high dimensionality, and inherent cellular heterogeneity. We present a novel integrative framework, called PseudoGRN, that unifies multiple pseudotime inference methods, different time-lagged divergence measures, non-redundant penalized network inference, and partial correlation analysis to reconstruct directed GRNs from time-series scRNA-seq data. Applying our method to the real-world scRNA-seq dataset, we demonstrate its superior performance over existing approaches, offering a robust and interpretable tool for uncovering dynamic regulatory mechanisms in single-cell systems.
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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.