Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks

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 diffusionbased "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. 1 1 This paper was selected for oral presentation at RECOMB 2015 and an abstract is published in the conference proceedings.

Prosnet: Integrating Homology With Molecular Networks for Protein Function Prediction

“…A key computational contribution is that ProSNet obtains low-dimensional vectors through a fast online learning algorithm instead of the batch learning algorithm used by previous work. 23,32 In each iteration, ProSNet samples a path from the heterogeneous network and optimizes low-dimensional vectors based on this path instead of all pairs of nodes. Therefore, it can easily scale to large networks containing hundreds of thousands or even millions of edges and nodes.…”

“…Like diffusion component analysis, 23 the number of pairs of nodes 〈 u , υ〉 that are connected by some path instances following at least one of the paths is O (| V | 2 ) in the worst case. This is too large for storage or processing when | V | is at the order of hundreds of thousands.…”

“…To transfer annotations within the same species, ProSNet first uses diffusion component analysis 23 on the Gene Ontology graph 2 to find low-dimensional vector y i for each functional label i . It then uses a transformation matrix W to project proteins from the protein vector space to the function vector space, which allows us to match proteins to functions based on geometric proximity.…”

“…22 Recently, this “guilt-by-association” principle has become the foundation of many network-based function prediction algorithms. 23–30 Among them, Gen-eMANIA 31 and clusDCA 32 are state-of-the-art network-based function prediction approaches. In addition to incorporating network topology, clusDCA also leverages the similarity between GO labels and obtains substantial improvement on sparsely annotated functions.…”

Large-scale integration of heterogeneous pharmacogenomic data for identifying drug mechanism of action

“…Mashup has been demonstrated to achieve significantly improved prediction for protein function prediction, gene ontology reconstruction, genetic interaction prediction, and drug-target interaction prediction. [20][21][22][23] It takes one or more networks as input, performs random walk with restart (RWR) 24 and extracts topological information from the diffusion distributions using informative but low-dimensional vector representations of drugs.…”