Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces CeRVE, a novel AI framework using directed graph neural networks to discover novel cancer biomarkers. CeRVE effectively predicts unknown cancer biomarker types by analyzing complex molecular interactions.
Area Of Science
- Computational biology
- Bioinformatics
- Cancer research
Background
- Discovering cancer biomarkers is crucial for disease understanding and treatment.
- Existing network-based methods often overlook novel association types due to reductionism.
- Integrating network structure with regulatory properties and validation remains challenging.
Purpose Of The Study
- To develop a multi-view learning framework (CeRVE) for predicting unknown cancer biomarker types.
- To overcome limitations of existing methods in discovering novel molecular associations.
- To provide an AI-assisted approach for cancer biomarker discovery.
Main Methods
- CeRVE framework utilizing directed graph neural networks (DGNN).
- Multi-view feature learning for subgraph information extraction and integration.
- DGNN simulation of regulatory networks to extract molecular interaction relationships.
- Differential expression analysis of mRNA, microRNA, and lncRNA using The Cancer Genome Atlas data.
Main Results
- CeRVE effectively extracts and integrates molecular subgraph information.
- DGNN simulates regulatory networks, revealing diverse molecular interactions.
- Identified potential cancer biomarkers across mRNA, microRNA, and lncRNA.
- Demonstrated superior performance in predicting biomarkers for 72 cancers.
Conclusions
- CeRVE offers a powerful AI-driven tool for cancer biomarker discovery.
- The framework successfully predicts unknown types of cancer biomarkers.
- Provides an insightful approach for advancing molecular association prediction in complex diseases.
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