HONeYBEE Framework Unlocks Next-Generation Cancer Research with Multimodal AI Integration

Revolutionizing Oncology Through Unified AI Platform

The HONeYBEE framework represents a significant leap forward in cancer research technology, enabling scalable multimodal artificial intelligence that bridges the gap between diverse data types in oncology. Unlike traditional single-modality approaches that struggle with the complexity of cancer data, this innovative platform provides standardized workflows for integrating clinical text, molecular profiles, pathology reports, whole-slide images, and radiologic images into unified patient representations.

Revolutionizing Oncology Through Unified AI Platform
Seamless Integration with Biomedical Infrastructure
Comprehensive Evaluation with Real-World Data
Advanced Foundation Models for Each Modality
Multimodal Integration Strategies
Practical Applications and Downstream Tasks
Accessibility and Community Impact

Seamless Integration with Biomedical Infrastructure

Designed for maximum interoperability, HONeYBEE connects directly with major biomedical data repositories including the NCI Cancer Research Data Commons (CRDC) ecosystem—encompassing Proteomics Data Commons (PDC), Genomic Data Commons (GDC), and Imaging Data Commons (IDC)—along with The Cancer Imaging Archive (TCIA). The framework’s compatibility with popular machine learning platforms like PyTorch, Hugging Face, and FAISS ensures researchers can leverage existing tools while benefiting from HONeYBEE’s advanced capabilities.

The platform includes pretrained foundation models and flexible pipelines for incorporating new models and modalities, addressing a critical limitation in current oncology AI tools. Where previous solutions required highly customized implementations for each data type, HONeYBEE offers standardized embedding workflows with minimal-code implementation, dramatically reducing the technical barrier for comprehensive cancer analysis.

Comprehensive Evaluation with Real-World Data

Researchers validated HONeYBEE using multimodal patient-level data from The Cancer Genome Atlas (TCGA), encompassing 11,428 patients across 33 cancer types. The dataset reflected real-world clinical constraints with heterogeneous, incomplete modality availability—clinical text (11,428 patients), molecular profiles (13,804 samples from 10,938 patients), pathology reports (11,108 patients), whole-slide images (8,060 patients), and radiologic images (1,149 patients)., as detailed analysis

This evaluation demonstrated HONeYBEE’s robustness in handling missing data, a common challenge in clinical research. The modular design accommodated patients with incomplete modality coverage without requiring complete-case cohorts, ensuring maximum data utilization while maintaining analytical integrity.

Advanced Foundation Models for Each Modality

HONeYBEE supports state-of-the-art foundation models for each data type, with the flexibility to integrate new models as they emerge:

Clinical Text & Pathology Reports: Multiple language models including GatorTron, Qwen3, Med-Gemma, and Llama-3.2, with GatorTron serving as the primary model for its specialized clinical text training
Whole-Slide Images: UNI (ViT-L/16), UNI2-h (ViT-g/14), and Virchow2 (DINOv2) models offering varying balances of efficiency and feature extraction capability
Radiological Imaging: RadImageNet CNN pre-trained on over four million medical images across CT, MRI, and PET modalities
Molecular Data: SeNMo, a self-normalizing deep learning encoder specifically designed for high-dimensional multi-omics data

Multimodal Integration Strategies

The framework implements three fusion approaches to combine information from available modalities: concatenation (preserving modality-specific information), mean pooling (averaging embeddings), and Kronecker product (capturing pairwise interactions between modalities). Surprisingly, evaluation revealed that clinical embeddings alone achieved the strongest cancer-type clustering with normalized mutual information of 0.7448 and adjusted mutual information of 0.702, outperforming both other single modalities and all multimodal fusion strategies.

This finding highlights the curated nature of clinical documentation in TCGA, where expert-extracted diagnostic variables effectively summarize information that might be dispersed across raw radiology, pathology, and molecular data. However, all three multimodal fusion approaches outperformed weaker single modalities such as molecular, radiology, and WSI embeddings, with concatenation achieving the best clustering performance among fusion methods.

Practical Applications and Downstream Tasks

HONeYBEE-generated embeddings demonstrated strong performance across critical oncology applications including cancer type classification, patient similarity retrieval, cancer-type clustering, and overall survival prediction. The platform’s ability to handle real-world data constraints while maintaining analytical precision positions it as a valuable tool for both research and potential clinical applications.

For survival analysis, researchers performed stratified evaluation across all 33 TCGA cancer types, training models individually for each cancer type using 5-fold cross-validation with stratification based on survival outcomes. This rigorous approach ensured robust performance assessment across diverse cancer contexts.

Accessibility and Community Impact

To accelerate adoption and collaboration, the HONeYBEE team has publicly released patient-level feature vectors and associated metadata through multiple Hugging Face repositories covering major cancer datasets including TCGA, CGCI, Foundation Medicine, CPTAC, and TARGET. This open approach facilitates broader research community engagement and enables independent validation of findings.

The framework’s design emphasizes practical implementation alongside technical innovation, addressing the critical need for tools that can bridge the gap between advanced AI capabilities and real-world clinical research constraints. By providing standardized workflows, flexible deployment options, and comprehensive multimodal integration, HONeYBEE represents a significant step toward scalable, reproducible cancer research using artificial intelligence.