HFR

Graph Attention Network

Multi-Omics Risk Stratification in Cancer Genomics

HFR Copyrighted Algorithm

Revolutionizing Cancer Risk Prediction

Our Graph Attention Network (GAT/GATv2) integrates multi-omics data with biological knowledge to predict patient survival outcomes and stratify risk groups with unprecedented accuracy.

Genomics AI-Powered Clinical Utility
Explore Technology

Key Performance Metrics

Clinical Validation

AUC 0.98

On selected biomarkers and stratification tasks

Multi-omic Fusion

Integrates genomics, proteomics, and clinical factors

Attention Mechanisms

Highlights influential pathways and interactions

Innovative Approach to Cancer Genomics

Heterogeneous Graph Architecture

Patient-Gene network with weighted edges from expression, mutation, CNV, and methylation data, enhanced with PPI/pathway knowledge.

GAT/GATv2 Multi-edge Types

Attention Mechanism

Dynamic attention weights identify influential gene interactions and pathways for interpretable risk prediction.

GATv2Conv Multi-head

Multi-Omics Integration

Combines gene expression, somatic variants, CNV, methylation, and clinical covariates into unified patient embeddings.

17D Features 2784 Genes

Clinical Risk Stratification

Predicts hazard scores, survival probabilities (1/3/5-year), and assigns patients to low/medium/high risk groups.

KM Curves C-index

Biomarker Discovery

Attention maps reveal 70 significant genes strongly associated with distinct survival outcomes.

Pathway Analysis p < 0.05

Production-Ready Pipeline

TorchScript/ONNX export, FastAPI microservice, and integration with HFR products (ACLIS, ML Copilot).

REST/gRPC Model Card

Multi-Omics Integration Workflow

Data Collection

TCGA multi-omics data (expression, mutations, CNV, methylation) + clinical covariates

Graph Construction

Patient-Gene heterogeneous graph with pathway-informed edges

GAT/GATv2 Training

Type-specific encoders with Cox partial likelihood loss

Risk Stratification

Patient embeddings → risk scores → clinically interpretable groups

Graph Architecture Details

  • Nodes: Patients P and Genes G (2784 cancer hallmark genes)
  • Edges: patient→gene with weights from multi-omics data + gene↔gene from PPI/pathways
  • Features: [expr_z, mut_bin, cnv_z, meth_z, mask] → linear projection
  • Readout: Patient embedding z_patient from final layer + optional attention pooling

Model Specifications

  • Framework: PyTorch + PyTorch Geometric (PyG)
  • Architecture: 2-3 GATv2 layers with residuals, layer-norm, dropout (0.2-0.4)
  • Optimizer: AdamW (lr=2e-4, weight decay 1e-4) with cosine decay
  • Regularization: Graph sparsity control, L1 on edge attentions, pathway-grouped dropout

Clinical Impact & Results

Survival Group Stratification

Group 0
154 months median survival
Group 1
49 months median survival
Group 2
21 months median survival

Log-rank test p < 0.05 for Group 2 vs Groups 0/1

Biomarker Discovery

Attention Threshold = 0.15 169 edges (138 genes)
Statistically Significant 70 edges (68 genes)

Top genes show AUC > 0.9 in classification tasks

Evaluation Metrics

0.98
Binary Classification AUC
(high-risk vs lower-risk)
0.93
Multi-class AUC
(3 risk groups)
0.85
Validation C-index
(concordance index)

Technology Stack

Python
PyTorch
PyG
TCGA
scikit-learn
pandas
numpy
matplotlib
seaborn
lifelines
FastAPI
GitHub

HFR Intellectual Property & Integration

Copyrighted Graph Algorithm

Proprietary methods for edge weighting, attention priors, and patient-gene subgraph construction.

  • Graph construction/learning components
  • Edge feature encoding
  • Attention mechanism enhancements

RAG for LLM

Retrieval-Augmented Generation system for literature grounding of top genes/pathways.

  • Evidence retrieval for oncogenic roles
  • Literature-augmented clinical notes
  • Integrated with attention explanations

HFR Product Integration

ACLIS

Clinical intelligence system integration via gRPC/REST with audit logs and governance protocols.

ML Copilot

Automated pipeline execution for experiments, sweeps, ablations, and model registry updates.

Research & Development

Our work on "Graph Attention Networks for Biomedical Insights: Multi-Omics Integration for Risk Stratification and Biomarker Identification" demonstrates the clinical utility of GNNs in cancer genomics.

Read Preprint View Code

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