Article

An Interpretable Sparse Graph Contrastive Learning Approach for Identifying Breast Cancer Risk Variants

2025-01-15

Abstract excerpt

<h4>SUMMARY</h4> Genome-wide association studies (GWASs) have identified over 2,400 genetic variants associated to breast cancer. Conventional GWASs methods that analyze variants independently often overlook the complex genetic interactions underlying disease susceptibility. Machine and deep learning approaches present promising alternatives, yet encounter challenges, including overfitting due to high dimensionali...

Topics

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

Identifiers and source

Literature Corpus work: 575d1b36-6650-5548-8933-4d2f97131287
DOI: 10.1101/2025.01.13.25320451

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

An Interpretable Sparse Graph Contrastive Learning Approach for Identifying Breast Cancer Risk VariantsDOI 10.1101/2025.01.13.25320451Read

GenoGraph: An Interpretable Graph Contrastive Learning Approach for Identifying Breast Cancer Risk VariantsSemantic proximity Read

A heterogeneous graph neural network for candidate gene prediction in endometriosis.Semantic proximity Read

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

Expression graph network framework for biomarker discoverySemantic proximity Read

Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA-gene-disease networks.Semantic proximity Read

Leveraging molecular-QTL co-association to predict novel disease-associated genetic loci using a graph convolutional neural networkSemantic proximity Read

PRSNet-2: End-to-end genotype-to-phenotype prediction via hierarchical graph neural networksSemantic proximity Read

Phenome-wide identification of therapeutic genetic targets, leveraging knowledge graphs, graph neural networks, and UK Biobank data.Semantic proximity Read

Predicting noncoding RNA and disease associations using multigraph contrastive learning.Semantic proximity Read

Small-cohort GWAS discovery with AI over massive functional genomics knowledge graphSemantic proximity Read

MTGCL: Multi-Task Graph Contrastive Learning for Identifying Cancer Driver Genes from Multi-omics DataSemantic proximity Read

Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.Semantic proximity Read