Gene networks modeling of microarray time series using Fuzzy Granger causality

Ensieh Nouri, Masoume Rahimi, M. Moradi
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引用次数: 1

Abstract

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.
基于模糊格兰杰因果关系的基因网络微阵列时间序列建模
宇宙中生物的生命,从细胞到社会,都是通过复杂的过程来保存生命的。了解基因网络,发现细胞内基因间的相互作用是生物系统研究的重要目标。基因网络的建模是基因水平信号处理中的重要问题之一。在微阵列技术发展之后,可以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利用利用格兰杰因果关系的微阵列时间序列数据对基因网络进行建模,并改进格兰杰因果关系,观察微阵列数据的模糊性,将格兰杰因果关系中的线性方法替换为模糊方法,分别应用于人工和真实的HELA数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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