Cytoscape is one of the most popular open-source software tools for the visual exploration of biomedical networks composed of protein, gene, and other types of interactions. It offers researchers a versatile and interactive visualization interface for exploring complex biological interconnections supported by diverse annotation and experimental data, thereby facilitating research tasks such as ...

Large biological networks contain much information about the functionality of protein-protein interactions and other macromolecular relationships. There are certain small subsets of these graphs which display similar characteristics that appear more frequently than others; these motifs can be used to uncover key information about the structure of the networks. Existing algorithms use exact iden...

Motivation: High-throughput methods for detecting molecular interactions have lead to a plethora of biological network data with much more yet to come, stimulating the development of techniques for biological network alignment. Analogous to sequence alignment, efficient and reliable network alignment methods will improve our understanding of biological systems. Network alignment is computationa...

Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approa...

For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH--an integration toolkit for building life science data ...

The rapid accumulation of biological network data is creating an urgent need for computational methods on integrative network analysis. Thus far, most such methods focused on the analysis of single biological networks. This chapter discusses a suite of methods we developed to mine patterns across many biological networks. Such patterns include frequent dense subgraphs, frequent dense vertex set...

A Bayesian network has often been modeled to infer a gene regulatory network from expression data. Genotypes along with gene expression can further reveal the regulatory relations and genetic architectures. Biological knowledge can also be incorporated to improve the reconstruction of a gene network. In this work, we propose a Bayesian framework to jointly infer a gene network and weights of pr...

We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 478...

Biological motifs are defined as overly recurring sub-patterns in biological systems. Sequence motifs and network motifs are the examples of biological motifs. Due to the wide range of applications, many algorithms and computational tools have been developed for efficient search for biological motifs. Therefore, there are more computationally derived motifs than experimentally validated motifs,...

Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big bi...