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The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest; it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our studied organism. Results also show that most of the essential proteins are close to other proteins, have assortativity behavior and form clusters/subgraph in the network. [ABSTRACT FROM AUTHOR]

The generation of protein-protein interactions (PPIs) has created the need for efficient computational approaches that can discover highly modular clusters of good quality. These clusters represent protein complexes or functional modules. There are a number of seed-growth style algorithms that exist to identify protein complexes from the genome-wide PPI networks. However, these methods lose accuracy when the networks are comparatively large and have complex connectivity. To combat the noise that exists in these large PPI networks, we propose an improvement to the graph entropy approach which is one of the seed-growth style algorithms. As a novel information-theoretic definition, Graph Entropy is a measure of the structural complexity of a graph. For example, the loss of entropy represents an increase in modularity of the graph. The original algorithm only considers the interconnected nature of vertices, but the new modified definition now considers edge weights. These edge weights are achieved by measuring the semantic similarity of PPIs. The weighted graph entropy approach is applied to the S. cerevisiae PPI data set from BioGRID. The output clusters are compared with known protein complexes so that we can calculate /-scores and use them to evaluate the clusters accuracy. The proposed improvement to the graph entropy approach proves to enhance the quality of clusters as potential protein complexes when compared to the other seed-growth style algorithms. [ABSTRACT FROM PUBLISHER]

In protein-protein interaction networks, proteins combine into macromolecular complexes to execute essential functions in the cells, such as replication, transcription, protein transport. To solve the problem of detecting protein complexes from protein interaction networks, we use relevant graph and irrelevant graph to represent the relation of connection between a node and a core graph. We define a variable Relevancy to represent whether a node has a dense or loose connection to a core graph. Then we propose the Relevancy Judgment algorithm to detecting protein complexes from protein interaction networks. Our algorithm decides whether a node belongs to a protein complex through judging the relevancy between core graph and nodes out of core graph. Experiment results show that our algorithm has an excellent performance in both accuracy and hit rate. [ABSTRACT FROM PUBLISHER]

Protein-protein interactions (PPIs) play a key role in understanding functional behavior of genes. Discovering association patterns from PPI networks is crucial for functional characterization on a system level. We present a novel approach to discover the functional association pattern of a query gene from the genome-wide PPI networks. This approach consists of two major components. First, we transform the PPI network to a weighted graph representation by measuring semantic similarity. Three enhanced semantic similarity methods are proposed to estimate functional closeness of each interacting pair. Second, we apply a dynamic propagation algorithm to detect the functional association pattern of a gene, represented as a sub-network. The size of the sub-networks is flexibly determined by user-specific parameters. In this paper, we also introduce an interactive web application, called M-Finder, to visualize the functional association pattern of a gene entered by a user. The semantic similarity measures and the dynamic propagation algorithm are embedded in this tool to run on up-to-date PPI networks of model species. M-Finder allows users to carry out further systematic analysis for functional characterization on the genomic scale. [ABSTRACT FROM PUBLISHER]