A polarity-aware multi-relational model for the signed interaction prediction in biological networks
arXiv:2407.07357v3 Announce Type: replace Abstract: Predicting signed interactions in biological networks is crucial for understanding drug mechanisms and facilitating drug repurposing. While deep graph models have demonstrated success in modeling complex biological systems, existing approaches often fail to distinguish between positive and negative interactions, limiting their utility for precise pharmacological predictions. In this study, we propose a novel deep graph model, PAMR (polarity-aware multi-relational model), designed to predict both polar (e.g., activation, inhibition) and non-po
The proliferation of deep learning techniques in biology and a growing demand for more precise drug discovery methods are driving advancements in predicting biological interactions.
Precise prediction of signed interactions in biological networks is critical for accelerating drug discovery, repurposing existing drugs, and understanding complex disease mechanisms.
This novel model introduces a more nuanced approach to biological network analysis by distinguishing positive and negative interactions, enhancing the utility of AI in pharmacological research.
- · Pharmaceutical companies
- · Biotech startups
- · AI drug discovery platforms
- · Medical researchers
- · Traditional drug screening methods
- · Drug discovery models lacking polarity analysis
More efficient and targeted drug candidate identification for various diseases.
Reduced R&D costs and faster time-to-market for new therapies and repurposed drugs.
Potential to unlock previously intractable drug targets and develop highly personalized medicines.
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Read at arXiv cs.LG