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Genetic programming-based approach to elucidate biochemical interaction networks from data

机译：基于遗传编程的方法，从数据中阐明生化相互作用网络

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摘要

Biochemical systems are characterised by cyclic/reversible reciprocal actions, non-linear interactions and a mixed relationship structures (linear and non-linear; static and dynamic). Deciphering the architecture of such systems using measured data to provide quantitative information regarding the nature of relationships that exist between the measured variables is a challenging proposition. Causality detection is one of the methodologies that are applied to elucidate biochemical networks from such data. Autoregressive-based modelling approach such as granger causality, partial directed coherence, directed transfer function and canonical variate analysis have been applied on different systems for deciphering such interactions, but with limited success. In this study, the authors propose a genetic programming-based causality detection (GPCD) methodology which blends evolutionary computation-based procedures along with parameter estimation methods to derive a mathematical model of the system. Application of the GPCD methodology on five data sets that contained the different challenges mentioned above indicated that GPCD performs better than the other methods in uncovering the exact structure with less false positives. On a glycolysis data set, GPCD was able to fill the 'interaction gaps' which were missed by other methods.
机译：生化系统的特征是循环/可逆的交互作用，非线性相互作用和混合关系结构（线性和非线性；静态和动态）。使用测得的数据破译此类系统的体系结构以提供有关测得的变量之间存在的关系的性质的定量信息是一项具有挑战性的提议。因果关系检测是用于从此类数据阐明生化网络的方法之一。基于自回归的建模方法（例如格兰杰因果关系，部分有向连贯性，有向传递函数和规范变量分析）已应用于不同的系统来解密此类交互，但是成功有限。在这项研究中，作者提出了一种基于遗传程序的因果关系检测（GPCD）方法，该方法将基于进化计算的过程与参数估计方法相结合，以得出系统的数学模型。 GPCD方法论在包含上述不同挑战的五个数据集上的应用表明，GPCD在发现准确率较低的准确结构方面比其他方法表现更好。在糖酵解数据集上，GPCD能够填补其他方法所遗漏的“相互作用缺口”。

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  • 来源
    《Systems Biology, IET》 |2013年第1期|18-25|共8页
  • 作者

    Kandpal M.; Kalyan C.M.; Samavedham L.;

  • 作者单位

    Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore|c|;

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