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The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.

Predicting protein function from protein interaction networks has been challenging because of the complexity of functional relationships among proteins. Most previous function prediction methods depend on the neighborhood of or the connected paths to known proteins. However, their accuracy has been limited due to the functional inconsistency of interacting proteins. In this paper, we propose a novel approach for function prediction by identifying frequent patterns of functional associations in a

M-Finder provides a useful framework to investigate functional association patterns with any protein. This software will also allow users to perform further systematic analysis of a set of proteins for any specific function. It is available online at http://bionet.ecs.baylor.edu/mfinder.

Supplementary data are available at Bioinformatics online.

Genome-wide association studies (GWAS) can be used to infer genome intervals that are involved in genetic diseases. However, investigating a large number of putative mutations for GWAS is resource- and time-intensive. Network-based computational approaches are being used for efficient disease-gene association prediction. Network-based methods are based on the underlying assumption that the genes causing the same diseases are located close to each other in a molecular network, such as a protein-p

Drug repositioning, which involves the identification of new therapeutic indications for approved drugs, considerably reduces the time and cost of developing new drugs. Recent computational drug repositioning methods use heterogeneous networks to identify drug-disease associations. This review reveals existing network-based approaches for predicting drug-disease associations in three major categories: graph mining, matrix factorization or completion, and deep learning. We selected eleven methods

Decomposing, converting, and synthesizing complex interaction networks are fundamental tasks for modeling their structural behaviors. In this study, we systematically analyzed complex interactome network structures for retrieving functional information. Unlike previous hierarchical clustering methods, this approach dynamically explores the hierarchical structure of proteins in a global view. It is well-applicable to the interactome networks in high-level organisms because of its efficiency and s

The experimentally determined protein-protein interactions are erroneous to uncover the functional associations among proteins. The performance of function prediction for uncharacterized proteins can be enhanced by the integration of multiple data sources available.

Recent computational analyses of protein interaction networks have attempted to understand cellular organizations, processes and functions. Various topology-based clustering methods have been applied to the protein interaction networks. However, they have been in difficulties due to unreliable interaction data and the specific features of the networks such as small-world and scale-free properties. In this paper, we present an information flow-based approach for analyzing the weighted protein int

A functional module has been defined as a group of molecules that participate in the same functional activities. Various graph-theoretic or data-mining techniques have been applied to discover functional modules from protein interaction networks [1]. However, their performance has been compromised by false-positive and false-negative interaction data and complex connectivity of the interaction networks. In our earlier study [2], we have introduced the functional flow-based approach to efficientl

Drug repositioning offers the significant advantage of greatly reducing the cost and time of drug discovery by identifying new therapeutic indications for existing drugs. In particular, computational approaches using networks in drug repositioning have attracted attention for inferring potential associations between drugs and diseases efficiently based on the network connectivity. In this article, we proposed a network-based drug repositioning method to construct a drug-gene-disease tensor by in

Recent computational analyses of protein interaction networks have attempted to understand cellular organizations, processes and functions. Several topology-based clustering methods have been applied to the protein interaction networks for detecting functional modules. However, most of the previous algorithms do not perform well on small-world, scale-free networks. In this paper, we present an efficient approach to identify hierarchical modules in the protein interaction networks. Our algorithm