A deep graph model for the signed interaction prediction in biological network

TL;DR

This paper introduces RGCNTD, a deep graph model that accurately predicts both polar and non-polar interactions in biological networks, enhancing drug discovery and pharmacology insights.

Contribution

The study presents a novel deep graph model combining graph convolution and tensor decomposition with conflict-aware sampling for improved interaction polarity prediction.

Findings

RGCNTD outperforms baseline models in accuracy.

The model effectively distinguishes interaction types.

Additional network structures do not always improve performance.

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, \textbf{RGCNTD} (Relational Graph Convolutional Network with Tensor Decomposition), designed to predict both polar (e.g., activation, inhibition) and non-polar (e.g., binding, affect) chemical-gene interactions. Our model integrates graph convolutional networks with tensor decomposition to enhance feature representation and incorporates a conflict-aware sampling strategy to resolve polarity ambiguities. We introduce new evaluation metrics, \textit{AUC\textsubscript{polarity}} and \textit{CP@500}, to assess the model's ability to differentiate interaction types. Experimental results demonstrate that \textbf{RGCNTD} outperforms baseline models, achieving superior classification accuracy and improved discrimination of polar edges. Furthermore, we analyze the impact of subgraph components on predictive performance, revealing that additional network structures do not always enhance accuracy. These findings highlight the importance of polarity-aware modeling in drug discovery and network pharmacology, providing a robust framework for predicting complex biological interactions.