Abstract
Biological networks obtained by high-throughput profiling or human curation are typically noisy. For functional module identification, single network clustering algorithms may not yield accurate and robust results. In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper. We construct an integrated network to combine network topological information based on protein-protein interaction (PPI) datasets and homological information introduced by constituent similarity between proteins across networks. A novel random walk strategy on the integrated network is developed for joint network clustering and an optimization problem is formulated by searching for low conductance sets defined on the derived transition matrix of the random walk, which fuses both topology and homology information. The optimization problem of joint clustering is solved by a derived spectral clustering algorithm. Network clustering using several state-of-the-art algorithms has been implemented to both PPI networks within the same species (two yeast PPI networks and two human PPI networks) and those from different species (a yeast PPI network and a human PPI network). Experimental results demonstrate that ASModel outperforms the existing single network clustering algorithms as well as another recent joint clustering algorithm in terms of complex prediction and Gene Ontology (GO) enrichment analysis.
Highlights
Over the past decade, one goal of systems biology is to understand how different molecules work together to maintain cellular functionalities [1,2]
We propose a joint clustering algorithm based on a new Markov random walk on an integrated network, which is constructed by integrating proteinprotein interactions in given protein-protein interaction (PPI) networks as well as homological interactions introduced by sequence similarity between proteins across networks
In addition to evaluating joint clustering by ASModel using synthetic networks, we evaluate the performances of ASModel, Normalized cut (NCut), Markov CLustering algorithm (MCL), Regularized MCL (RMCL), and AlignNemo on public PPI datasets for S. cerevisiae and H. sapiens
Summary
One goal of systems biology is to understand how different molecules work together to maintain cellular functionalities [1,2]. The formulation of NCut is equivalent to finding low conductance sets on the transition matrix of the Markov random walk on the network to analyze [12,13]. We propose a joint clustering algorithm based on a new Markov random walk on an integrated network, which is constructed by integrating proteinprotein interactions in given PPI networks as well as homological interactions introduced by sequence similarity between proteins across networks. A novel alternative random walk strategy is proposed on the integrated network with the transition matrix integrating both topology and homology information. The organization of the rest of the paper is as follows: In section 2, we introduce the construction of the integrated network, the new random walk strategy, our final optimization problem formulation and the spectral algorithm for joint clustering.
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