Modeling, inference and optimization of regulatory networks based on time series data

Weber, Gerhard‐Wilhelm; Defterli, Özlem; Gök, Sırma Zeynep Alparslan; Kropat, Erik

doi:10.1016/j.ejor.2010.06.038

Cited by 51 publications

(17 citation statements)

References 31 publications

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“…In general, it can be considered as an optimization problem to infer the biological systems (Chen et al, 2010;Kikuchi et al, 2003;Liu & Wang, 2008;Tsai & Wang, 2005;Weber et al, 2011). For example, Weber et al (2011) proposed an improved modeling of gene-environment networks with analyses of uncertainty and constraints on complexity and regularization via different optimization techniques. However, most of the existing studies focus on the only estimation of either structures or the parameters, and it is difficult to guarantee the obtained mathematical model can optimally describe the biological system.…”

Section: Mopath For Biological Systemmentioning

confidence: 99%

“…The main task of modeling a biological system is to systematically understand its natural characteristics and predict its dynamic behavior. However, it is difficult to give exact mathematical equations of the complex processes in biological systems, such as gene regulation networks (Gebert, Radde, & Weber, 2007;Weber, Defterli, Gökc, & Kropat, 2011;Xu, 2013), metabolic pathways (Schuster, Fell, & Dandekar, 2000;Zazopoulos et al, 2003), and signal transduction cascades (Acharya, Judeh, Duan, Rabbat, & Zhu, 2000;Chen & Wu, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling nonlinear dynamic biological systems with human-readable fuzzy rules optimized by convergent heterogeneous particle swarm

Cheung

Ding

et al. 2015

European Journal of Operational Research

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Section: Mopath For Biological Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling nonlinear dynamic biological systems with human-readable fuzzy rules optimized by convergent heterogeneous particle swarm

Cheung

Ding

et al. 2015

European Journal of Operational Research

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“…Furthermore, driven by profits, benefits and other demands, investors or governments will never cease to pursue better performing models, especially in the context of energy or finance. Since many of the existing models listed above cannot perform well enough in complex, realistic situations, some researchers have recently investigated other possible solutions [26]- [30].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation

Liu

Xiao

Lin

et al. 2020

IEEE Trans. Fuzzy Syst.

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Forecasting time series is an emerging topic in operational research. Existing time series models have limited prediction accuracy when faced with the characteristics of nonlinearity and nonstationarity in complex situations related to energy and finance. To enhance overall prediction capabilities and improve forecasting accuracy, we propose a fuzzy interval time series forecasting model on the basis of network-based multiple time-frequency spaces and the induced ordered weighted averaging aggregation (IOWA) operation. Specifically, a time series signal is decomposed into ensemble empirical modes and then reconstructed as various time-frequency spaces, which are transformed into visibility graphs. Then, forecasting intervals in different spaces can be collected after the local random walker link prediction model is adopted. Furthermore, a rule-based representation value function inspired by Yager's golden rule approach is defined, and an appropriate representation value is calculated. Finally, after IOWA is used to aggregate the forecasting outcomes in different time-frequency spaces, the final forecast value can be obtained from the fuzzy forecasting interval. Considering that energy issues are of widespread interest in nature and the social economy, two cases, based on a hydrological time series from the Biliuhe River in China and two wellknown sets of financial time series data, TAIEX and HSI, are studied to test the performance of the proposed approach in comparison with existing models. Our results show that the proposed approach can achieve better performance than well-developed models.

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“…This type of methods simulates exactly the linear structure of the differential equations. Defterli et al [ 24 ] and Weber et al [ 25 ] considered discretizing and optimizing the so-called gene-environment networks based on usually finite data series.…”

Section: Introductionmentioning

confidence: 99%

Steady-State-Preserving Simulation of Genetic Regulatory Systems

Zhang

Ehigie

Hou

et al. 2017

Computational and Mathematical Methods in Medicine

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A novel family of exponential Runge-Kutta (expRK) methods are designed incorporating the stable steady-state structure of genetic regulatory systems. A natural and convenient approach to constructing new expRK methods on the base of traditional RK methods is provided. In the numerical integration of the one-gene, two-gene, and p53-mdm2 regulatory systems, the new expRK methods are shown to be more accurate than their prototype RK methods. Moreover, for nonstiff genetic regulatory systems, the expRK methods are more efficient than some traditional exponential RK integrators in the scientific literature.

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Modeling, inference and optimization of regulatory networks based on time series data

Cited by 51 publications

References 31 publications

Modeling nonlinear dynamic biological systems with human-readable fuzzy rules optimized by convergent heterogeneous particle swarm

Modeling nonlinear dynamic biological systems with human-readable fuzzy rules optimized by convergent heterogeneous particle swarm

A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation

Steady-State-Preserving Simulation of Genetic Regulatory Systems

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