My Virtual Cancer
Leili ShahriyariMohamed H. Abdel‐RahmanAlireza AsadpoureColleen M. Cebullaying xian changWenrui HaoPamela JacksonA. LeeNavid Mohammad MirzaeiDaniel G. StoverIoannis K. Zervantonakis
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Since each cancer has its own unique characteristics, each one can respond differently to the same treatments. Therefore, the creation of a digital twin (DT) of cancer can assist us in predicting the evolution of an individual's cancer through modeling each tumor's characteristics and response to treatment. Hence, we propose to take advantage of new advances in computational approaches and combine mechanistic, machine learning, and stochastic modeling approaches to create “My Virtual Cancer", a DT platform. To establish a personalized DT, we use patient-specific data for parameter estimations, sensitivity analysis, and uncertainty quantification. For each patient, we will estimate the values of parameters of their QSP model using the patient's data. We perform a multi-dimensional sensitivity analysis and uncertainty quantification on the mechanistic model to find a set of critical interactions and predict the intervals of confidence. Since this QSP model includes the data-driven mechanistic model of cells and molecules' interaction networks, one of the ultimate results of this DT would be the prediction of evolution of tumors.According to the reservoir sensitivity evaluation standard,analysis the Chang 6 reservoir sensitivity.The results show that chang 6 reservoir belong to weak velocity sensitivity、medium salt sensitivity,Non-alkali sensitivity、weak water sensitivity and weak acid sensitivity.
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The sensitivity of parameters in computational science problems is difficult to assess, especially for algorithms with multiple input parameters and diverse outputs. This work seeks to explore sensitivity analysis in the visualization domain, introducing novel techniques for respective visual analyses of parameter sensitivity in multi-dimensional algorithms. First, the sensitivity analysis background is revisited, highlighting the definition of sensitivity analysis and approaches analyzing global and local sensitivity as well as the differences of sensitivity analysis to the more common uncertainty analysis. We introduce and explore parameter sensitivity using visualization techniques from overviews to details on demand, covering the analysis of all aspects of sensitivity in a prototypical implementation. The respective visualization techniques outline the algorithmic in- and outputs including indications, on how sensitive an input is with regard to the outputs. The detailed sensitivity information is communicated through constellation plots for the exploration of input and output spaces. A matrix view is discussed for localized information on the sensitivity of specific outputs to specific inputs. A 3D view provides the link of the parameter sensitivity to the spatial domain, in which the results of the multi-dimensional algorithms are embedded. The proposed sensitivity analysis techniques are implemented and evaluated in a prototype called Sensitivity Explorer. We show that Sensitivity Explorer reliably identifies the most influential parameters and provides insights into which of the output characteristics these affect as well as to which extent.
Parallel coordinates
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Variance-based sensitivity analysis
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Based on the theory of sensitivity analysis,the local/global sensitivity analysis of parameters for the modeling of thermal transport in aquifer under special groundwater flow and heat source conditions was carried out with the summer running of GWHP system as a study case,by taking the temperature of typical observation points and thermal affected zone (TAZ) of injecting water at the end of summer as the model outputs. The results showed that the sensitivity of each modeling output was various for the different parameter in local analysis. The orders of local sensitivity coefficients of the nine parameters for two modeling outputs were almost coincident. In global sensitivity analysis,the sensitivity of the parameter being investigated was influenced by the different values of other parameters. The two modeling outputs almost had the same variation tendency of global sensitivity with different parameter combinations. The numerical model was stable.
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e-sensitivity analysis is a kind of methods for performing sensitivity analysis for linear programming. Its main advantage is that it can be directly applied for interior-point methods with a little computation. Although e-sensitivity analysis was proposed several years ago, there have been no studies on its relationship with other sensitivity analysis methods. In this paper, we discuss the relationship between e-sensitivity analysis and sensitivity analysis using an optimal basis. First, we present a property of e-sensitivity analysis, from which we derive a simplified formula for finding the characteristic region of e-sensitivity analysis. Next, using the simplified formula, we examine the relationship between e-sensitivity analysis and sensitivity analysis using optimal basis when an e-optimal solution is sufficiently close to an optimal extreme solution. We show that under primal nondegeneracy or dual nondegeneracy of an optimal extreme solution, the characteristic region of e-sensitivity analysis converges to that of sensitivity analysis using an optimal basis. However, for the case of both primal and dual degeneracy, we present an example in which the characteristic region of e-sensitivity analysis is different from that of sensitivity analysis using an optimal basis.
Basis (linear algebra)
Variance-based sensitivity analysis
Degeneracy (biology)
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The third-order optimazed design is mainly used to revise the old network,which aims at decreasing the field work under the precision and the reliability of the network.The weakest position is probed by sensitivity in this paper.The new concept of comprehensive sensitivity is put forward and compared with precision sensitivity and reliable sensitivity with examples.The conclusions are:the third-order optimazed design with reliable sensitivity is better than precision sensitivity;and comprehensive sensitivity is effective to find the weakest position of the network.
Position (finance)
Third order
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In order to well conduct the optimal selection of parameters of distributed hydrological model efficiently,and eliminate the uncertainty in the calculation process and understand the influence of the parameters on the hydrological process better,the sensitivity of the parameters is analyzed.The LH-OAT method was applied to the parameter sensitivity analysis of Liuxihe River Model,a distributed physical hydrological model,in three basins under multiple object functions.The sensitivity indices of the parameters can be divided into 4 classes,including high sensitivity,sensitivity,general sensitivity and non-sensitivity.It is shown that the parameter sensitivity of the model is not immutable,but changes with different basins or different evaluation objects.
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