Why probability and statistics?

Dekking, F. M.; Kraaikamp, Cor; Lopuhaä, Hendrik P.; Meester, Ludolf Erwin

doi:10.1007/1-84628-168-7_1

Cited by 66 publications

(37 citation statements)

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“…We begin with a characterization of the distributions for all networks composed of metabolic genes (≈1.25 M possible pairs) as well as our supplemental corresponding networks for which we examine only known cancer related genes (≈100 K possible pairs). In particular, we provide an analysis in terms of average curvature, the difference in expected value on upper/lower 5% tails of distribution along with the p-value result of a paired one-tailed Wilcoxon signed-rank test 25 ( Table 1 , Table S1 ). This analysis is done, in part, to characterize the shift of distribution with respect to (cancer-normal) changes in Ollivier-Ricci curvature.…”

Section: Resultsmentioning

confidence: 99%

Graph Curvature for Differentiating Cancer Networks

Sandhu

Georgiou

Reznik

et al. 2015

Sci Rep

154

227

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Cellular interactions can be modeled as complex dynamical systems represented by weighted graphs. The functionality of such networks, including measures of robustness, reliability, performance, and efficiency, are intrinsically tied to the topology and geometry of the underlying graph. Utilizing recently proposed geometric notions of curvature on weighted graphs, we investigate the features of gene co-expression networks derived from large-scale genomic studies of cancer. We find that the curvature of these networks reliably distinguishes between cancer and normal samples, with cancer networks exhibiting higher curvature than their normal counterparts. We establish a quantitative relationship between our findings and prior investigations of network entropy. Furthermore, we demonstrate how our approach yields additional, non-trivial pair-wise (i.e. gene-gene) interactions which may be disrupted in cancer samples. The mathematical formulation of our approach yields an exact solution to calculating pair-wise changes in curvature which was computationally infeasible using prior methods. As such, our findings lay the foundation for an analytical approach to studying complex biological networks.

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Section: Resultsmentioning

confidence: 99%

Graph Curvature for Differentiating Cancer Networks

Sandhu

Georgiou

Reznik

et al. 2015

Sci Rep

154

227

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“…The accuracies of the approximations of the temperature and humidity records are compared with accuracies of the approximations using the pure LS approach. For a more precise comparison, the “root mean squared errors” (RMSE) of the temperature and humidity approximations are calculated for each sensor node using the same actual values that were used in the sliding backpropagation approximation [ 42 ].

…”

Section: Resultsmentioning

confidence: 99%

Application of Neurocomputing for Data Approximation and Classification in Wireless Sensor Networks

Jabbari

Jedermann

Muthuraman

et al. 2009

Sensors

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A new application of neurocomputing for data approximation and classification is introduced to process data in a wireless sensor network. For this purpose, a simplified dynamic sliding backpropagation algorithm is implemented on a wireless sensor network for transportation applications. It is able to approximate temperature and humidity in sensor nodes. In addition, two architectures of “radial basis function” (RBF) classifiers are introduced with probabilistic features for data classification in sensor nodes. The applied approximation and classification algorithms could be used in similar applications for data processing in embedded systems.

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“…As shown in the table, the percentage of actions in the classes are close to what one would expect when assuming the (log)normal distribution. The deviations are caused by the fact that these response times were monitored in a real environment, rather than a perfect environment without external influences [26]. Figure 8 shows the distribution of SARatio in the EOL case study, together with the 85 th and 95 th percentile.…”

Section: Exact Onlinementioning

confidence: 99%

Performance optimization of deployed software-as-a-service applications

Bezemer

Zaidman

2014

Journal of Systems and Software

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The goal of performance maintenance is to improve the performance of a software system after delivery. As the performance of a system is often characterized by unexpected combinations of metric values, manual analysis of performance is hard in complex systems. In this paper, we propose an approach that helps performance experts locate and analyze spots -so called performance improvement opportunities (PIOs) -, for possible performance improvements. PIOs give performance experts a starting point for performance improvements, e.g., by pinpointing the bottleneck component. The technique uses a combination of association rules and performance counters to generate the rule coverage matrix, a matrix which assists with the bottleneck detection.In this paper, we evaluate our technique in two cases studies. In the first, we show that our technique is accurate in detecting the timeframe during which a PIO occurs. In the second, we show that the starting point given by our approach is indeed useful and assists a performance expert in diagnosing the bottleneck component in a system with high precision.

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Why probability and statistics?

Cited by 66 publications

References 0 publications

Graph Curvature for Differentiating Cancer Networks

Graph Curvature for Differentiating Cancer Networks

Application of Neurocomputing for Data Approximation and Classification in Wireless Sensor Networks

Performance optimization of deployed software-as-a-service applications

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