Evidence map›Paper›PMID 42321873›Full record

ArticleGenome biology2026

GenOT: generative optimal transport enables spatiotemporal interpolation and generation in cross-platform spatial transcriptomics.

Rui Wang, Xinxin Liu, Linlin Zhuo, Zhecheng Zhou, Junlin Xu, Quan Zou, Xiangzheng Fu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Rui Wang *School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325000, China.
Xinxin Liu *School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325000, China.
Linlin ZhuoSchool of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325000, China. 20210339@wzut.edu.cn.
Zhecheng ZhouCenter for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Science, Macao Polytechnic University, Macao, 999078, China.
Junlin XuSchool of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, China.
Quan ZouInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 611730, China. zouquan@nclab.net.
Xiangzheng FuFaculty of Computer Science and Artificial Intelligence, University of Advanced Technology, 518107, Shenzhen, China. fxz326@hnu.edu.cn.

Funding

National Natural Science Foundation of China 6237215National Natural Science Foundation of China 62572352
6 · The paper itself

Abstract

Spatial transcriptomics technologies have revolutionized the analysis of spatial gene expression, yet integrating spatial information and generating data across heterogeneous samples remain challenging. We present GenOT, a generative framework combining multi-scale graph self-supervised contrastive learning with optimal transport barycenter theory for efficient cross-slice and cross-platform spatiotemporal interpolation. The core innovation of GenOT lies in introducing an optimal transport barycenter-based interpolation algorithm, which mathematically models spatial distribution differences across heterogeneous samples to reconstruct spatiotemporal gene expression dynamics. Extensive evaluations demonstrate that GenOT consistently outperforms existing approaches in spatial domain identification, cross-platform interpolation, and developmental trajectory reconstruction.

Indexed as

AlgorithmsGene Expression ProfilingSpatial TranscriptomicsTranscriptomeAnimalsGraph Neural NetworksSpatio-Temporal AnalysisBarycenterFused Gromov-Wasserstein distanceGraph neural networksOptimal transportSpatial transcriptomicsSpatiotemporal interpolation

Identifiers

PMID42321873
PMCPMC13531826

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.