Transformation of Biological Networks into Images via Semantic Cartography for Visual Interpretation and Scalable Deep Analysis

TL;DR

Graph2Image transforms large biological networks into images, enabling scalable deep learning analysis with improved accuracy and interpretability, and supports multimodal integration for biomedical research.

Contribution

The paper introduces Graph2Image, a novel framework converting biological networks into images to enhance scalability, interpretability, and multimodal analysis using CNNs.

Findings

Achieved up to 67.2% accuracy improvement over existing methods.

Enabled analysis of networks with over 1 billion nodes on a personal computer.

Provided biologically coherent visualizations of network patterns.

Abstract

Complex biological networks are fundamental to biomedical science, capturing interactions among molecules, cells, genes, and tissues. Deciphering these networks is critical for understanding health and disease, yet their scale and complexity represent a daunting challenge for current computational methods. Traditional biological network analysis methods, including deep learning approaches, while powerful, face inherent challenges such as limited scalability, oversmoothing long-range dependencies, difficulty in multimodal integration, expressivity bounds, and poor interpretability. We present Graph2Image, a framework that transforms large biological networks into sets of two-dimensional images by spatially arranging representative network nodes on a 2D grid. This transformation decouples the nodes as images, enabling the use of convolutional neural networks (CNNs) with global receptive fields and multi-scale pyramids, thus overcoming limitations of existing biological network analysis methods in scalability, memory efficiency, and long-range context capture. Graph2Image also facilitates seamless integration with other imaging and omics modalities and enhances interpretability through direct visualization of node-associated images. When applied to several large-scale biological network datasets, Graph2Image improved classification accuracy by up to 67.2% over existing methods and provided interpretable visualizations that revealed biologically coherent patterns. It also allows analysis of very large biological networks (nodes > 1 billion) on a personal computer. Graph2Image thus provides a scalable, interpretable, and multimodal-ready approach for biological network analysis, offering new opportunities for disease diagnosis and the study of complex biological systems.