Network compression as a quality measure for protein interaction networks.

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks.

Drug repositioning through incomplete bi-cliques in an integrated drug-target-disease network.

Recently, there has been much interest in gene-disease networks and polypharmacology as a basis for drug repositioning. Here, we integrate data from structural and chemical databases to create a drug-target-disease network for 147 promiscuous drugs, their 553 protein targets, and 44 disease indications. Visualizing and analyzing such complex networks is still an open problem.

Unraveling protein networks with power graph analysis.

Networks play a crucial role in computational biology, yet their analysis and representation is still an open problem. Power Graph Analysis is a lossless transformation of biological networks into a compact, less redundant representation, exploiting the abundance of cliques and bicliques as elementary topological motifs. We demonstrate with five examples the advantages of Power Graph Analysis.