Article

Predictive modelling of highly multiplexed tumour tissue images by graph neural networks

2021-07-30

Abstract excerpt

The progression and treatment response of cancer largely depends on the complex tissue structure that surrounds cancer cells in a tumour, known as the tumour microenvironment (TME). Recent technical advances have led to the development of highly multiplexed imaging techniques such as Imaging Mass Cytometry (IMC), which capture the complexity of the TME by producing spatial tissue maps of dozens of proteins. Combin...

Topics

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

Identifiers and source

Literature Corpus work: 32d3b296-1a9c-575d-b061-d0ba135bd5a2
DOI: 10.1101/2021.07.28.21261179

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Predictive modelling of highly multiplexed tumour tissue images by graph neural networksDOI 10.1101/2021.07.28.21261179Read

Deep multimodal graph-based network for survival prediction from highly multiplexed images and patient variables.Semantic proximity Read

Deep Multimodal Graph-Based Network for Survival Prediction from Highly Multiplexed Images and Patient VariablesSemantic proximity Read

Bi-level Graph Learning Unveils Prognosis-Relevant Tumor Microenvironment Patterns in Breast Multiplexed Digital PathologySemantic proximity Read

Expression graph network framework for biomarker discoverySemantic proximity Read

Decoding mTOR signalling heterogeneity in the tumour microenvironment using multiplexed imaging and graph convolutional networksSemantic proximity Read

Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE SlidesSemantic proximity Read

Multiplexed tumor profiling with generative AI accelerates histopathology workflows and improves clinical predictionsSemantic proximity Read

Graph attention-based fusion of pathology images and gene expression for prediction of cancer survivalSemantic proximity Read

Spatially aware graph neural networks and cross-level molecular profile prediction in colon cancer histopathology: a retrospective multi-cohort studySemantic proximity Read

DeepMOCCA: A pan-cancer prognostic model identifies personalized prognostic markers through graph attention and multi-omics data integrationSemantic proximity Read

StereoMM: A Graph Fusion Model for Integrating Spatial Transcriptomic Data and Pathological ImagesSemantic proximity Read

Spatial Morphoproteomic Features Predict Uniqueness of Immune Microarchitectures and Responses in Lymphoid FolliclesSemantic proximity Read