Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks

TL;DR

This paper introduces diffusion component analysis (DCA), a novel method that combines diffusion models with dimensionality reduction to improve gene function prediction in biological networks, handling noisy and incomplete data effectively.

Contribution

The paper presents DCA, a new framework that learns low-dimensional node representations from diffusion processes, enhancing function prediction and enabling integration of multiple heterogeneous networks.

Findings

DCA outperforms existing diffusion-based methods in protein function prediction.

Integrating multiple networks improves prediction accuracy.

Combining DCA with SVMs further enhances performance.

Abstract

Complex biological systems have been successfully modeled by biochemical and genetic interaction networks, typically gathered from high-throughput (HTP) data. These networks can be used to infer functional relationships between genes or proteins. Using the intuition that the topological role of a gene in a network relates to its biological function, local or diffusion based "guilt-by-association" and graph-theoretic methods have had success in inferring gene functions. Here we seek to improve function prediction by integrating diffusion-based methods with a novel dimensionality reduction technique to overcome the incomplete and noisy nature of network data. In this paper, we introduce diffusion component analysis (DCA), a framework that plugs in a diffusion model and learns a low-dimensional vector representation of each node to encode the topological properties of a network. As a proof of concept, we demonstrate DCA's substantial improvement over state-of-the-art diffusion-based approaches in predicting protein function from molecular interaction networks. Moreover, our DCA framework can integrate multiple networks from heterogeneous sources, consisting of genomic information, biochemical experiments and other resources, to even further improve function prediction. Yet another layer of performance gain is achieved by integrating the DCA framework with support vector machines that take our node vector representations as features. Overall, our DCA framework provides a novel representation of nodes in a network that can be used as a plug-in architecture to other machine learning algorithms to decipher topological properties of and obtain novel insights into interactomes.