EPSILON: an eQTL prioritization framework using similarity measures derived from local networks

Motivation: When genomic data are associated with gene expression data, the resulting expression quantitative trait loci (eQTL) will likely span multiple genes. eQTL prioritization techniques can be used to select the most likely causal gene affecting the expression of a target gene from a list of candidates. As an input, these techniques use physical interaction networks that often contain highly connected genes and unreliable or irrelevant interactions that can interfere with the prioritization process. We present EPSILON, an extendable framework for eQTL prioritization, which mitigates the effect of highly connected genes and unreliable interactions by constructing a local network before a network-based similarity measure is applied to select the true causal gene. Results: We tested the new method on three eQTL datasets derived from yeast data using three different association techniques. A physical interaction network was constructed, and each eQTL in each dataset was prioritized using the EPSILON approach: first, a local network was constructed using a k-trials shortest path algorithm, followed by the calculation of a network-based similarity measure. Three similarity measures were evaluated: random walks, the Laplacian Exponential Diffusion kernel and the Regularized Commute-Time kernel. The aim was to predict knockout interactions from a yeast knockout compendium. EPSILON outperformed two reference prioritization methods, random assignment and shortest path prioritization. Next, we found that using a local network significantly increased prioritization performance in terms of predicted knockout pairs when compared with using exactly the same network similarity measures on the global network, with an average increase in prioritization performance of 8 percentage points (P < 10(-5))

Packetized-voice/data integrated transmission on a token passing ring local area network

This paper investigates the performance of a token passing ring network with packetized-voice/data mixed traffic through extensive simulations. Both data and voice users are modeled in the simulations. Data users produce bursty traffic. Voice traffic is modeled as having alternating talkspurts and silences, with generation of voice packets at a constant rate during talkspurts and no packet generation during silence periods.The network performance measures obtained include: the distribution of transmission delays for voice packets, the average transmission delay and loss probabilities for voice packets, the number of voice users allowed on a network while satisfying the real-time constraints of speech, and the average transmission delay for data packets.Token passing ring local area networks are shown to effectively handle both voice and data traffic. The effects of system parameters (e.g., voice packet length, talkspurt/silence lengths, data traffic intensity, and limited versus exhaustive service disciplines) on network performance are discussed

Analysis of CIM performance using different LAN structures a simulation approach

This research illustrates a systematic procedure for modeling and performance analysis of the integration effect of communication network to the physical system. The concept is to model different layouts of Computer Integration Manufacturing (CIM) using different Local Area Network(LAN) structures. The steps to accomplish this concepts are, a) To determine the performance measures for physical layouts and the communication network, in order to obtain a performance analysis. b) Modeling the physical layout using Promodel simulation package. c) Extracting results from the outcome of the simulation of the physical layout and using this as input to the communication network simulation. d) Modeling the communication network using LNET simulation package. e) Comparing the output of each simulation run and determine which is most acceptable. Having different performance measures for both physical layout and networks, the proposed research objective is to illustrate the effectiveness of network structures on physical systems performance. Throughput, utilization, and delay are used as measures for both the physical layouts and network structures. Using these measures the optimum layout and network is selected

Edge vulnerability in neural and metabolic networks

Biological networks, such as cellular metabolic pathways or networks of corticocortical connections in the brain, are intricately organized, yet remarkably robust toward structural damage. Whereas many studies have investigated specific aspects of robustness, such as molecular mechanisms of repair, this article focuses more generally on how local structural features in networks may give rise to their global stability. In many networks the failure of single connections may be more likely than the extinction of entire nodes, yet no analysis of edge importance (edge vulnerability) has been provided so far for biological networks. We tested several measures for identifying vulnerable edges and compared their prediction performance in biological and artificial networks. Among the tested measures, edge frequency in all shortest paths of a network yielded a particularly high correlation with vulnerability, and identified inter-cluster connections in biological but not in random and scale-free benchmark networks. We discuss different local and global network patterns and the edge vulnerability resulting from them.Comment: 8 pages, 4 figures, to appear in Biological Cybernetic

Network Model Selection Using Task-Focused Minimum Description Length

Networks are fundamental models for data used in practically every application domain. In most instances, several implicit or explicit choices about the network definition impact the translation of underlying data to a network representation, and the subsequent question(s) about the underlying system being represented. Users of downstream network data may not even be aware of these choices or their impacts. We propose a task-focused network model selection methodology which addresses several key challenges. Our approach constructs network models from underlying data and uses minimum description length (MDL) criteria for selection. Our methodology measures efficiency, a general and comparable measure of the network's performance of a local (i.e. node-level) predictive task of interest. Selection on efficiency favors parsimonious (e.g. sparse) models to avoid overfitting and can be applied across arbitrary tasks and representations. We show stability, sensitivity, and significance testing in our methodology

Stitching together the fabric of space and society: an investigation into the linkage of the local to regional continuum

To date, space syntax models have focused typically on relatively small areas up to the city scale. There have been very few models that take into account the entire network up to the regional scale, so the cumulative effects of micro-scale connections on regional networks is unknown, and the performance of the regional network as a function of the local area cannot be assessed. As such, a complete understanding of the ways in which regional centres are co-dependent and cities relate to their surrounding sub-centres is lacking. This study models the entire road network at the regional scale, by dispensing with axial lines entirely and moving to a road-centre line model of the UK, the Ordnance Survey's Integrated Transport Network (ITN) layer. This layer includes the topological connections between roads, so that a complete topological model of the road network including the directionality of streets can be constructed quickly. A region of the North of England - including Manchester, Bradford, Sheffield and Leeds - is analysed. Regional level angular analysis is shown to correlate well with overall movement in the network, while local level metric analysis is shown to correlate with the population density. It is hypothesised that combined measures that link the global to the local will uncover discontinuities in the continuum of space, and that these disruptions to the network will correspond to social deprivation. However, although such discontinuities exist, experimental linkage of the analysis to deprivation indices by census areas shows little conclusive evidence. In particular, it is clear that the complex web of spatial factors uncovered need investigation with more sensitive tools and smaller units of aggregation. The study highlights the need for a set of combined measures using microscopic spatial, economic, demographic, and land-use data, in order to further understand the relationship of spatial factors with social activity, while reinforcing standard space syntax results at the regional level

The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition

A critical feature of the human brain that gives rise to complex cognition is its ability to reconfigure its network structure dynamically and adaptively in response to the environment. Existing research probing task-related reconfiguration of brain network structure has concluded that, although there are many similarities in network structure during an intrinsic, resting state and during the performance of a variety of cognitive tasks, there are meaningful differences as well. In this study, we related intrinsic, resting state network organization to reconfigured network organization during the performance of two tasks: a sequence tapping task, which is thought to probe motor execution and likely engages a single brain network, and an n-back task, which is thought to probe working memory and likely requires coordination across multiple networks. We implemented graph theoretical analyses using functional connectivity data from fMRI scans to calculate whole-brain measures of network organization in healthy young adults. We focused on quantifying measures of network segregation (modularity, system segregation, local efficiency, number of provincial hub nodes) and measures of network integration (global efficiency, number of connector hub nodes). Using these measures, we found converging evidence that local, within-network communication is critical for motor execution, whereas integrative, between-network communication is critical for working memory. These results confirm that the human brain has the remarkable ability to reconfigure its large-scale organization dynamically in response to current cognitive demands and that interpreting reconfiguration in terms of network segregation and integration may shed light on the optimal network structures underlying successful cognition

Coherence in Large-Scale Networks: Dimension-Dependent Limitations of Local Feedback

We consider distributed consensus and vehicular formation control problems. Specifically we address the question of whether local feedback is sufficient to maintain coherence in large-scale networks subject to stochastic disturbances. We define macroscopic performance measures which are global quantities that capture the notion of coherence; a notion of global order that quantifies how closely the formation resembles a solid object. We consider how these measures scale asymptotically with network size in the topologies of regular lattices in 1, 2 and higher dimensions, with vehicular platoons corresponding to the 1 dimensional case. A common phenomenon appears where a higher spatial dimension implies a more favorable scaling of coherence measures, with a dimensions of 3 being necessary to achieve coherence in consensus and vehicular formations under certain conditions. In particular, we show that it is impossible to have large coherent one dimensional vehicular platoons with only local feedback. We analyze these effects in terms of the underlying energetic modes of motion, showing that they take the form of large temporal and spatial scales resulting in an accordion-like motion of formations. A conclusion can be drawn that in low spatial dimensions, local feedback is unable to regulate large-scale disturbances, but it can in higher spatial dimensions. This phenomenon is distinct from, and unrelated to string instability issues which are commonly encountered in control problems for automated highways.Comment: To appear in IEEE Trans. Automat. Control; 15 pages, 2 figure