ReviewiMeta2026
Spatial omics in the AI era: Technologies, algorithmic ecosystems, biological applications, and large model perspectives.
Review in iMeta, 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
27 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial omics technologys help overcome key limitations of conventional omics approaches that lack spatial information, by providing a panoramic perspective from the molecular level to the microenvironment scale for addressing spatially resolved biological questions in life sciences. With the rapid advancement of this field, there are significant differences among technology platforms, algorithms, and research workflows, which bring three core challenges to interdisciplinary researchers: the detailed explanation of technical principles, the selection of appropriate algorithms, and the future development directions. This review systematically summarizes the technical platforms and analytical algorithms of spatial omics, compares their advantages and disadvantages in the context of specific tasks and presents application cases across multiple biological fields. It also outlines the emerging research directions and advances in large model integration. It ultimately aims to provide a reference for researchers from diverse disciplines to design and implement spatial omics studies.
Indexed as
Identifiers
What Socratic 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 Socratic graph.