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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm
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Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm

机译：基于神经网络的基于神经网络基因监管网络使用大象群水搜索算法

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摘要

Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of diffrerent noise levels and the effciency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very effcient for GRN inference problem and performs better than other methods in many ways.
机译：从生物数据库中正确推断遗传法规，如时间序列微阵列数据是生物学家和研究人员后基因组时代的最大挑战之一。经常性神经网络（RNN）是模拟动态的最受欢迎和最简单的方法之一，以及在基因之间推断正确的依赖性。灵感来自社会大象的行为，我们提出了一个新的常规即大象群水搜索算法（ESWSA）来推断基因监管网络（GRN）。该算法主要基于干旱期间智能和社会大象的水搜索策略，利用不同类型的通信技术。最初，该算法针对基准中小学人工遗传网络测试而没有并且存在衍射噪声水平的存在，并且在参数误差期间观察到效率，最小健身值，执行时间，真正调节预测准确性等。接下来，测试该算法针对大肠杆菌SOS网络的实时基因表达数据测试，并且还与其他技术的优化方法的结果进行了比较。实验结果表明，ESWSA对于GRN推理问题非常有效，并且在许多方面比其他方法更好地执行。

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