Article

A Graph Neural Network Approach for Hierarchical Mapping of Breast Cancer Protein Communities

2024-06-11

Abstract excerpt

<title>Abstract</title> <p>Background Comprehensively mapping the hierarchical structure of breast cancer protein communities and identifying potential biomarkers from them is a promising way for breast cancer research. Existing approaches are subjective and fail to take information from protein sequences into consideration. Deep learning can automatically learn features from protein sequences and protein-protei...

Topics

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

Identifiers and source

Literature Corpus work: 92d32423-86d5-59de-8ac6-3d1dbcf57ecb
DOI: 10.21203/rs.3.rs-4478708/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A Graph Neural Network Approach for Hierarchical Mapping of Breast Cancer Protein CommunitiesDOI 10.21203/rs.3.rs-4478708/v1Read

Structural deep clustering network for stratification of breast cancer patients through integration of somatic mutation profiles.Semantic proximity Read

DeepGraphMut: A graph-based deep learning method for cancer prognosis using somatic mutation profileSemantic proximity Read

Prior knowledge informs graph neural networks to improve phenotype prediction from proteomicsSemantic proximity Read

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

Graph-Regularised Multi-Omics Autoencoders Improve Stability and Clinical Coherence of Breast Cancer Patient EmbeddingsSemantic proximity Read

Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSOSemantic proximity Read

Discovering Disease Genes in PPI Networks: A Bridge from Centrality to CommunitiesSemantic proximity Read

Expression graph network framework for biomarker discoverySemantic proximity Read

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

Network-aware mutation clustering of cancerSemantic proximity Read

CAERUS: predicting CAncER oUtcomeS using relationship between protein structural information, protein networks, gene expression data, and mutation data.Semantic proximity Read

Patient-Specific Analysis of Co-expression Networks for Predicting Clinical Outcomes in Breast CancerSemantic proximity Read