PuSH - Publication Server of Helmholtz Zentrum München: Multi-manifolds fusing hyperbolic graph network balanced by pareto optimization for identifying spatial domains of spatial transcriptomics.

Information

Clues to quality of journals

Open Access Policy of Helmholtz Association 2016

CC Licencences

Publication Metrics

Navigation

Home

Deutsch

EVA: Electronic Publishing

New EVA Application

Research

Advanced Search

Browse by ...

... HMGU-Authors/Consortia

... Organizational Structure

... Journal

... Publication Type

... Research Data

... Arbeitsgruppen

... Publication Year

Publication overview

Statistics

Statistics (last 5 years)

OA Publications

Publish

Submit Publication

Import publication from...

...EVA

Report missing publication

Highlights

Support & Contact

Contact persons

Help

Data protection

Helmholtz Open Science

JANE Journal Estimator

SHERPA/RoMEO

DOAJ

Export:

Text

Endnote (RIS) BIB

BibTeX

Li, Y.* ; Hu, Q.* ; Han, S.* ; Wang-Sattler, R. ; Du, W.*

Multi-manifolds fusing hyperbolic graph network balanced by pareto optimization for identifying spatial domains of spatial transcriptomics.

Brief. Bioinform. 26:bbaf162 (2025)

Publ. Version/Full Text

DOI

PMC

	Open Access Gold

Abstract
Metrics
Extra information

Identifying spatial domains for spatial transcriptomics is crucial for achieving comprehensive insights into the pathogenesis of gene expression. Increasingly, computational methods based on graph neural networks are being developed for spatial transcriptomics. However, previous methods have solely focused on the Euclidean manifold. To effectively exploit and explore the informative and deeper topological structures of inherent manifolds, we presented a Multi-Manifolds fusing hyperbolic graph network, balanced by Pareto optimization, for identifying spatial domains in Spatial Transcriptomics (MManiST). First, we developed multi-manifolds encoders for distinct manifolds using the hyperbolic neural network. Features from different manifolds were then combined using an attention mechanism, with multiple reconstruction losses balanced by Pareto optimization. Extensive experiments on commonly used benchmark datasets show that our method consistently outperforms seven state-of-the-art methods. Additionally, we investigated the validity of each component and the impact of fusion methods in ablation experiments.

Impact Factor

Scopus SNIP

Altmetric

7.700

0.000