Back to search

Article

SPgen: Proteome-wide Spatial Proteomics generation using multi-modality foundation models

2026-07-21

Abstract excerpt

Spatial proteomics (SP) measures the spatial distribution of proteins within tissues, providing important insights into tissue function, disease, and therapeutic response. However, current SP technologies profile only a small fraction of the proteome and are limited by cost and measurement noise. Recent AI approaches enable predicting spatial protein expression from transcriptomic or histopathological data, but ar...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
2a28b6fe-1d02-5584-a159-12699f47ba2e
DOI
10.64898/2026.07.16.739037
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
SPgen: Proteome-wide Spatial Proteomics generation using multi-modality foundation modelsDOI 10.64898/2026.07.16.739037
SpatialMOC: Accurate reconstruction of spatial multi-omics landscapes through cross-modality predictionSemantic proximity
Phenotype-associated spatial biomarker discovery in spatial transcriptomics with spHOTSemantic proximity
spEMO: Leveraging Multi-Modal Foundation Models for Analyzing Spatial Multi-Omic and Histopathology DataSemantic proximity
spEMO: Leveraging Multi-Modal Foundation Models for Analyzing Spatial Multi-Omic and Histopathology DataSemantic proximity
Spatial-DC: a robust deep learning-based method for deconvolution of spatial proteomicsSemantic proximity
Transferable spatial omics deconvolution with SpaRankSemantic proximity
NicheTrans: Spatial-aware Cross-omics TranslationSemantic proximity
scGPT-spatial: Continual Pretraining of Single-Cell Foundation Model for Spatial TranscriptomicsSemantic proximity
AGP-Net: A Universal Network for Gene Expression Prediction of Spatial TranscriptomicsSemantic proximity
SR2P: an efficient stacking method to predict protein abundance from gene expression in spatial transcriptomics dataSemantic proximity
SpaPheno: linking spatial transcriptomics to clinical phenotypes with interpretable machine learning.Semantic proximity
SpaPheno: Linking Spatial Transcriptomics to Clinical Phenotypes with Interpretable Machine LearningSemantic proximity
Select a neighboring publication to make it the new centre.