Academic

Distilling and Adapting: A Topology-Aware Framework for Zero-Shot Interaction Prediction in Multiplex Biological Networks

arXiv:2603.06618v1 Announce Type: new Abstract: Multiplex Biological Networks (MBNs), which represent multiple interaction types between entities, are crucial for understanding complex biological systems. Yet, existing methods often inadequately model multiplexity, struggle to integrate structural and sequence information, and face difficulties in zero-shot prediction for unseen entities with no prior neighbourhood information. To address these limitations, we propose a novel framework for zero-shot interaction prediction in MBNs by leveraging context-aware representation learning and knowledge distillation. Our approach leverages domain-specific foundation models to generate enriched embeddings, introduces a topology-aware graph tokenizer to capture multiplexity and higher-order connectivity, and employs contrastive learning to align embeddings across modalities. A teacher-student distillation strategy further enables robust zero-shot generalization. Experimental results demonstrate

A
Alana Deng, Sugitha Janarthanan, Yan Sun, Zihao Jing, Pingzhao Hu · · 1 min read · 12 views
#cs.LG #cs.AI #q-bio.QM

arXiv:2603.06618v1 Announce Type: new Abstract: Multiplex Biological Networks (MBNs), which represent multiple interaction types between entities, are crucial for understanding complex biological systems. Yet, existing methods often inadequately model multiplexity, struggle to integrate structural and sequence information, and face difficulties in zero-shot prediction for unseen entities with no prior neighbourhood information. To address these limitations, we propose a novel framework for zero-shot interaction prediction in MBNs by leveraging context-aware representation learning and knowledge distillation. Our approach leverages domain-specific foundation models to generate enriched embeddings, introduces a topology-aware graph tokenizer to capture multiplexity and higher-order connectivity, and employs contrastive learning to align embeddings across modalities. A teacher-student distillation strategy further enables robust zero-shot generalization. Experimental results demonstrate that our framework outperforms state-of-the-art methods in interaction prediction for MBNs, providing a powerful tool for exploring various biological interactions and advancing personalized therapeutics.

Executive Summary

The proposed framework addresses limitations in existing methods for zero-shot interaction prediction in Multiplex Biological Networks (MBNs) by leveraging context-aware representation learning, knowledge distillation, and topology-aware graph tokenization. The approach integrates structural and sequence information, enabling robust zero-shot generalization and outperforming state-of-the-art methods. This framework has the potential to advance personalized therapeutics by providing a powerful tool for exploring various biological interactions.

Key Points

  • ▸ Novel framework for zero-shot interaction prediction in MBNs
  • ▸ Leverages context-aware representation learning and knowledge distillation
  • ▸ Introduces topology-aware graph tokenizer to capture multiplexity and higher-order connectivity

Merits

Improved Accuracy

The framework demonstrates improved accuracy in interaction prediction for MBNs, outperforming state-of-the-art methods.

Robust Zero-Shot Generalization

The teacher-student distillation strategy enables robust zero-shot generalization, allowing for prediction of unseen entities with no prior neighborhood information.

Demerits

Computational Complexity

The framework's reliance on domain-specific foundation models and contrastive learning may increase computational complexity and require significant resources.

Expert Commentary

The proposed framework represents a significant advancement in the field of biological network analysis, addressing key limitations in existing methods and demonstrating improved accuracy and robust zero-shot generalization. The integration of context-aware representation learning, knowledge distillation, and topology-aware graph tokenization provides a powerful tool for exploring various biological interactions. However, the framework's computational complexity and reliance on domain-specific foundation models may require careful consideration and optimization for practical applications.

Recommendations

  • ✓ Further evaluation of the framework's performance on diverse biological networks and interaction types
  • ✓ Investigation of the framework's potential applications in other fields, such as social network analysis and recommender systems

Sources

Related Articles