yet whether the scale free features are generic to all biological networks due to the limited availability of information on pathways and connectivity. To overcome these limitations and to expand the knowledge of biological network topology, we propose a novel method for network topology inference

of interactions to distill tissue specific networks. Through comparing our tissue specific networks with those inferred from gene expression data, we found our networks have larger scales and higher reliability. Furthermore, we investigated the similar extent of multiple tissue specific networks, which proved

In this paper, we examine the asymptotic behavior of degree correlation (i.e., the joint degree distribution of adjacent nodes) in several scale-free topology generators GED [14], PLRG [1], GLP [11], BA [4], AB [2]. We present a unifying analytical framework that allows tractable analysis of degree

between these two sources of order, natural selection and physical self-organization, in the evolution of human gene regulation. The topology of a human gene coexpression network, derived from tissue-specific expression profiles, shows scale-free properties that imply evolutionary selforganization via

analysis of Open Source Software developers by studying the entire development community at SourceForge [26]. Statistics and social network properties are explored to find collaborations and the effects of different members in the OSS development community. Small world phenomenon and scale free behaviors

and filtered to generate scale-free, hierarchical networks that are statistically significant and biologically relevant. The networks are validated with known gene interactions and used to predict regulatory pathways important for the developing mammalian heart. Area under the precision-recall curve

is based on the Sequential Floating Forward Selection (SFFS) algorithm, with the inclusion of a scale-free (Barabási-Albert) topology information in order to guide the search process to improve inference. The proposed algorithm explores the scale-free property by pruning the search space and using a power

The application of network analysis to emergent mating topologies in spatially structured genetic algorithms is presented in this preliminary study as a framework for inferring evolutionary dynamics in recombinant evolutionary search. Emergent mating topologies of populations evolving on regular

that transmits and distributes muscle forces to finger joints. This network is critical to the versatility of the human hand, and its function has been debated since at least the 16 th century. Here, we experimentally infer the structure (both topology and parameter values) of this network through sparse

network inference, we design a novel Metropolis-Hastings sampler for graphical models that includes a node labeling as a state space variable. In a simulation study, we demonstrate that the scale-free structure prior outperforms the random structure prior at recovering scale-free networks while