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Network alignment is a computational technique to identify topological similarity of graph data by mapping link patterns. In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionarily conserved substructures at the system level. In particular, local network alignment of PPI networks searches for conserved functional components...
Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there...
Advanced technologies are producing large-scale protein-protein interaction data at an ever increasing pace. Finding protein-protein interaction complexes from large PPI networks is a fundamental problem in bioinformatics. In order to solve the set contains false negative and false positive noise data of protein data, we develop the graph model for limitation of structural protein complex. We proposed...
In recent years, numerous protein-protein interaction (PPI) datasets have been generated with the development of high-throughput experimental techniques. These datasets enable researchers to uncover protein complexes on network level. However, the performance of the computational methods relies heavily on the quality of the underlying protein interaction data, and these datasets are usually quite...
Density modularity can overcome this defect, but it use Simulated Annealing (SA) algorithm to search the maximal density modularity, which can't ensure to rapidly search the global optimal solution of problem. Based on this consideration, we propose a Closely Associated Degree (CAD) algorithm to discover protein functional module which continuously improve density modularity of PPI network. CAD first...
In order to overcome the limitations of global modularity and the deficiency of local modularity, we introduce a hybrid modularity measure LGQ (Local-Global Quantification) which adopts a suitable modularity adjustable parameter to control the balance of global detecting capability and local search capability in Protein-Protein Interaction (PPI) network. On the other hand, a new protein complex mining...
A signaling pathway, which is represented as a chain of interacting proteins for a biological process, can be predicted from protein-protein interaction (PPI) networks. However, pathway prediction is computationally challenging because of (1) inefficiency in searching all possible paths from the large-scale PPI networks and (2) unreliability of current PPI data generated by automated high-throughput...
The experimental study of signal transduction over a decade has made a substantial contribution to understanding functional mechanisms in a cell. A signaling pathway represents a linear path of a signaling cascade involving a series of proteins. As an advanced model, multiple linear pathways with extensive cross-talk between receptors can be merged into a larger-scale signaling network. We present...
In this paper, we present a method based on mining maximal frequent patterns for core-attachment complexes identification in yeast protein-protein interaction networks (PINs). Our method contains of two stages. Firstly, it finds all the protein-complex cores by mining maximal frequent patterns in PIN using FP-growth method. Then it filters the redundant cores and adds the attachment proteins for each...
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