ReviewNature reviews. Genetics2026
Decoding epithelial-mesenchymal transitions with multi-omics.
Review in Nature reviews. Genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epithelial-mesenchymal transition (EMT) is a highly dynamic and plastic cellular programme essential for embryonic development, tissue repair and ageing, and it is co-opted in pathological conditions such as cancer and fibrosis. The continuum of intermediate states and pronounced cellular heterogeneity that characterize EMT have historically limited systematic investigation across regulatory layers, including the transcriptome, epigenome, proteome and metabolome. Recent advances in high-throughput omics technologies, particularly single-cell and spatially resolved platforms, have enabled comprehensive profiling of cellular states at unprecedented resolution. These approaches, in parallel with computational methodologies such as machine learning frameworks, network-based modelling and integrative multi-omics, have been instrumental in extracting biological insight from intrinsically complex datasets. In this Review, we discuss how the convergence of emerging omics technologies and computational frameworks has advanced EMT research, outline key analytical considerations and discuss current challenges and future opportunities for biomarker discovery and translational applications.
Identifiers
42834121What 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.