ArticleBriefings in bioinformatics2025
STIFT: spatiotemporal transcriptomics integration through spatially informed multi-timepoint bridging.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Machine learning and computational approaches to model therapeutic response and resistance in diffuse midline glioma.Journal of neuro-oncology · 2026Review
- ScSpTITH: a rank-correlation framework for robust quantification of multi-dimensional tumor heterogeneity.Human genetics · 2026Article
- AI-driven multiscale virtual plant cell modeling: from molecular mechanisms to tissue functions.Planta · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Recent advances in spatial transcriptomics have highlighted the need for integrating spatiotemporal transcriptomics data, defined as spatially resolved gene expression profiles captured across sequential time points in developmental or regenerative processes. We present STIFT (SpatioTemporal Integration Framework for Transcriptomics) specifically designed for integrating spatiotemporal transcriptomics data. STIFT is a three-component framework combining developmental spatiotemporal optimal transport, spatiotemporal graph construction, and a graph attention autoencoder informed by temporal triplet learning. STIFT integrates large-scale 2D or 3D spatiotemporal transcriptomics data, enabling batch effect removal, spatial domain identification, trajectory inference and exploration of developmental dynamics. Applied to axolotl brain regeneration, mouse embryonic development, and 3D planarian regeneration datasets, STIFT removes batch effects and achieves clear spatial domain identification while preserving temporal developmental patterns and biological variations across hundreds of thousands of spots, demonstrating its effectiveness and specificity in integrating spatiotemporal transcriptomics data.
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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.