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Gene networks modeling of microarray time series using Fuzzy Granger causality

机译:基于模糊格兰杰因果关系的微阵列时间序列基因网络建模

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The life of living beings from cell to society in the universe is controlled by complex processes to preserve life. Understanding the gene network and discovering interactions between genes in cells is an important goal in biological systems. Modeling the gene network is one of the important issues in signal processing at the gene level. After the development of microarray technology, it was possible to model this network using time series data. The main objective of this research is to model the gene network from microarray time-series data that uses Granger causality, and to improve Granger causality and to observe the vague nature of microarray data,The linear method in Granger causality is replaced by a fuzzy method which then was applied on artificial and the real HELA data.
机译:宇宙中从细胞到社会的生物生命受到复杂的过程控制,以保护生命。了解基因网络并发现细胞中基因之间的相互作用是生物系统的重要目标。基因网络建模是基因水平信号处理中的重要问题之一。随着微阵列技术的发展,有可能使用时间序列数据对该网络进行建模。本研究的主要目的是从使用Granger因果关系的微阵列时间序列数据建模基因网络,并改善Granger因果关系并观察微阵列数据的模糊性质,将Granger因果关系中的线性方法替换为模糊方法然后将其应用于人工和真实HELA数据。

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