Advances in protein-protein interaction (PPI) detection technology and computational analysis methods have produced numerous PPI networks, whose completeness appears to depend on the extent of data derived from different PPI assay methods and the complexity of the studied organism. Despite the partial nature of human PPI networks, computational data integration and analyses helped to elucidate ...

Protein-protein interaction (PPI) networks are naturally viewed as infrastructure to infer signalling pathways. The descriptors of signal events between two interacting proteins such as upstream/downstream signal flow, activation/inhibition relationship and protein modification are indispensable for inferring signalling pathways from PPI networks. However, such descriptors are not available in ...

Graph clustering algorithms are widely used in the analysis of biological networks. Extracting functional modules in protein-protein interaction (PPI) networks is one such use. Most clustering algorithms whose focuses are on finding functional modules try either to find a clique like sub networks or to grow clusters starting from vertices with high degrees as seeds. These algorithms do not make...

The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction (PPI) network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network. However, to date, these methods have used a static pict...

In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfull...

MOTIVATION Tumor necrosis factor-alpha (TNF-α), a major inflammatory cytokine, is closely related to several cardiovascular pathological processes. However, its effects on the cell cycle of vascular endothelial cells (VECs) have been the subject of some controversy. To investigate the molecular mechanism underlying this process, we constructed time-course protein-protein interaction (PPI) netwo...

Many real-world phenomena have been described in terms of large networks. Networks have been invaluable models for the understanding of biological systems. Since proteins carry out most biological processes, we focus on analysing protein-protein interaction (PPI) networks. Proteins interact to perform a function. Thus, PPI networks reflect the interconnected nature of biological processes and a...

The objective of this study was to identify disrupted pathways in teratozoospermia by systematically tracking dysregulated modules in reweighted protein-protein interaction (PPI) networks. We inferred and reweighted the PPI networks of normal and teratozoospermia groups based on Spearman correlation coefficients. Modules in the PPI networks were explored via a clique-merging algorithm and alter...

Analysis of Protein-Protein Interaction (PPI) networks is of great significance in evolutionary biology. Because of high computation cost, recently multi-PPI network alignment becomes hot topic. In this paper, we proposed conserved topological structures mining based multiPPI network alignment technology. The most challenging problems in conserved topological structure mining are the large size...

MOTIVATION Time-evolving differential protein-protein interaction (PPI) networks are essential to understand serial activation of differentially regulated (up- or downregulated) cellular processes (DRPs) and their interplays over time. Despite developments in the network inference, current methods are still limited in identifying temporal transition of structures of PPI networks, DRPs associate...